{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('../data/train_xy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('../data/train_x.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15000, 160)\n",
      "(10000, 159)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x = train.drop(['y'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 159)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test = pd.concat([train_x,test])\n",
    "train_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>cust_group</th>\n",
       "      <th>x_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>x_5</th>\n",
       "      <th>x_6</th>\n",
       "      <th>x_7</th>\n",
       "      <th>x_8</th>\n",
       "      <th>...</th>\n",
       "      <th>x_148</th>\n",
       "      <th>x_149</th>\n",
       "      <th>x_150</th>\n",
       "      <th>x_151</th>\n",
       "      <th>x_152</th>\n",
       "      <th>x_153</th>\n",
       "      <th>x_154</th>\n",
       "      <th>x_155</th>\n",
       "      <th>x_156</th>\n",
       "      <th>x_157</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110000</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.604988</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110001</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.012058</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110002</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.565979</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110003</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.316209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110004</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.008061</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 159 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cust_id cust_group       x_1       x_2  x_3  x_4  x_5  x_6  x_7  x_8  \\\n",
       "0   110000    group_3  0.354167  0.604988  -99  -99  -99  -99  -99  -99   \n",
       "1   110001    group_3  0.125000  0.012058  -99  -99  -99  -99  -99  -99   \n",
       "2   110002    group_3  0.333333  0.565979    0    0    0    0    0    0   \n",
       "3   110003    group_3  0.208333  0.316209    0    0    0    0    1    1   \n",
       "4   110004    group_3  0.208333  0.008061  -99  -99  -99  -99  -99  -99   \n",
       "\n",
       "   ...    x_148  x_149  x_150  x_151  x_152  x_153  x_154  x_155  x_156  x_157  \n",
       "0  ...        1      1      1      1      1      1      1      1      3    -99  \n",
       "1  ...        1      1      1      1      1      1      1      1      2      2  \n",
       "2  ...        1      1      2      1      1      1      1      1      2      2  \n",
       "3  ...        2      1      1      1      1      1      1      1      2      4  \n",
       "4  ...        1      1      1      1      1      1      1      1      2      1  \n",
       "\n",
       "[5 rows x 159 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-a4e8703700b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m25000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[1;32mif\u001b[0m \u001b[0mtrain_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m99\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m             \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mnumber\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1470\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1471\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   2011\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2013\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2014\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2015\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    220\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Too many indexers'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 raise ValueError(\"Location based indexing can only have \"\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1955\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1956\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1957\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1958\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1959\u001b[0m             \u001b[1;31m# a tuple should already have been caught by this point\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   2007\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2009\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2010\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2011\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "#缺失值统计\n",
    "number = []\n",
    "count = 0\n",
    "for i in range(2,159):\n",
    "    print(i)\n",
    "    for j in range(25000):\n",
    "        if train_test.iloc[j,i] == -99:\n",
    "            count = count + 1\n",
    "    number.append(count)\n",
    "    count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c455b4ba20>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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v82o7aeWn5IMdL2TCHfXlsXYGHkg+MWjvsnxX2seefUuMm4CfkO80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\nSZIkSZIkSZKkCf0//tHB2W6xiPsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2520x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nu = pd.DataFrame(number)\n",
    "nu.plot.bar(figsize=(35,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_drop_col = train.drop(['x_92', 'x_94', 'x_102', 'x_103', 'x_104', 'x_105', 'x_106', 'x_107', 'x_108', 'x_109', 'x_110', 'x_111', 'x_112', 'x_113', 'x_114', 'x_115', 'x_116', 'x_117', 'x_118', 'x_119', 'x_120', 'x_121', 'x_122', 'x_123', 'x_124', 'x_125', 'x_126', 'x_127', 'x_128', 'x_129', 'x_130', 'x_131', 'x_132', 'x_133', 'x_134', 'x_135', 'x_136', 'x_137', 'x_138'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15000, 121)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_drop_col.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cust_id', 'cust_group', 'y', 'x_1', 'x_2', 'x_3', 'x_4', 'x_5',\n",
       "       'x_6', 'x_7', 'x_8', 'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14',\n",
       "       'x_15', 'x_16', 'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22',\n",
       "       'x_23', 'x_24', 'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30',\n",
       "       'x_31', 'x_32', 'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38',\n",
       "       'x_39', 'x_40', 'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46',\n",
       "       'x_47', 'x_48', 'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54',\n",
       "       'x_55', 'x_56', 'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62',\n",
       "       'x_63', 'x_64', 'x_65', 'x_66', 'x_67', 'x_68', 'x_69', 'x_70',\n",
       "       'x_71', 'x_72', 'x_73', 'x_74', 'x_75', 'x_76', 'x_77', 'x_78',\n",
       "       'x_79', 'x_80', 'x_81', 'x_82', 'x_83', 'x_84', 'x_85', 'x_86',\n",
       "       'x_87', 'x_88', 'x_89', 'x_90', 'x_91', 'x_93', 'x_95', 'x_96',\n",
       "       'x_97', 'x_98', 'x_99', 'x_100', 'x_101', 'x_139', 'x_140',\n",
       "       'x_141', 'x_142', 'x_143', 'x_144', 'x_145', 'x_146', 'x_147',\n",
       "       'x_148', 'x_149', 'x_150', 'x_151', 'x_152', 'x_153', 'x_154',\n",
       "       'x_155', 'x_156', 'x_157'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_drop_col.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_drop_col = test.drop(['x_92', 'x_94', 'x_102', 'x_103', 'x_104', 'x_105', 'x_106', 'x_107', 'x_108', 'x_109', 'x_110', 'x_111', 'x_112', 'x_113', 'x_114', 'x_115', 'x_116', 'x_117', 'x_118', 'x_119', 'x_120', 'x_121', 'x_122', 'x_123', 'x_124', 'x_125', 'x_126', 'x_127', 'x_128', 'x_129', 'x_130', 'x_131', 'x_132', 'x_133', 'x_134', 'x_135', 'x_136', 'x_137', 'x_138'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 120)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_drop_col.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n",
      "128\n",
      "128\n",
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      "128\n",
      "128\n",
      "149\n",
      "128\n",
      "0\n",
      "146\n",
      "46\n",
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      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "77\n",
      "677\n"
     ]
    }
   ],
   "source": [
    "miss_all = []\n",
    "for i in range(39,92):\n",
    "    col = 'x'+'_'+str(i)\n",
    "    miss = train_drop_col[train_drop_col[col]== -99].index.values\n",
    "    print(len(miss))\n",
    "    miss_all.append(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149\n",
      "149\n",
      "149\n",
      "149\n"
     ]
    }
   ],
   "source": [
    "miss_all1 = []\n",
    "for i in range(97,101):\n",
    "    col = 'x'+'_'+str(i)\n",
    "    miss = train_drop_col[train_drop_col[col]== -99].index.values\n",
    "    print(len(miss))\n",
    "    miss_all1.append(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "46\n",
      "475\n",
      "77\n",
      "77\n"
     ]
    }
   ],
   "source": [
    "miss_all2 = []\n",
    "for i in range(143,156):\n",
    "    col = 'x'+'_'+str(i)\n",
    "    miss = train_drop_col[train_drop_col[col]== -99].index.values\n",
    "    print(len(miss))\n",
    "    miss_all2.append(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "con1 = miss_all1[0]\n",
    "count = 0\n",
    "for i in range(1,4):\n",
    "    con1 = np.concatenate((con1,miss_all1[i]),axis = 0)    \n",
    "drop_row1 = np.unique(con1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "con2 = miss_all2[0]\n",
    "count = 0\n",
    "for i in range(1,13):\n",
    "    con2 = np.concatenate((con2,miss_all2[i]),axis = 0)    \n",
    "drop_row2 = np.unique(con2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "con = miss_all[0]\n",
    "count = 0\n",
    "for i in range(1,53):\n",
    "    con = np.concatenate((con,miss_all[i]),axis = 0)    \n",
    "drop_row = np.unique(con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(966,)\n",
      "(149,)\n",
      "(572,)\n"
     ]
    }
   ],
   "source": [
    "print(drop_row.shape)\n",
    "print(drop_row1.shape)\n",
    "print(drop_row2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1398,)\n"
     ]
    }
   ],
   "source": [
    "drop_row_con = np.concatenate((drop_row,drop_row1,drop_row2),axis = 0) \n",
    "drop_row_all = np.unique(drop_row_con)\n",
    "\n",
    "print(drop_row_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15000, 121)\n",
      "(13602, 121)\n"
     ]
    }
   ],
   "source": [
    "train_drop_col_row = train_drop_col.drop(drop_row_all)\n",
    "print(train_drop_col.shape)\n",
    "print(train_drop_col_row.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>cust_group</th>\n",
       "      <th>y</th>\n",
       "      <th>x_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>x_5</th>\n",
       "      <th>x_6</th>\n",
       "      <th>x_7</th>\n",
       "      <th>...</th>\n",
       "      <th>x_148</th>\n",
       "      <th>x_149</th>\n",
       "      <th>x_150</th>\n",
       "      <th>x_151</th>\n",
       "      <th>x_152</th>\n",
       "      <th>x_153</th>\n",
       "      <th>x_154</th>\n",
       "      <th>x_155</th>\n",
       "      <th>x_156</th>\n",
       "      <th>x_157</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110000</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.604988</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110001</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.012058</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110002</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.565979</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110003</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.316209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110004</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.008061</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cust_id cust_group  y       x_1       x_2  x_3  x_4  x_5  x_6  x_7  ...    \\\n",
       "0   110000    group_3  0  0.354167  0.604988  -99  -99  -99  -99  -99  ...     \n",
       "1   110001    group_3  0  0.125000  0.012058  -99  -99  -99  -99  -99  ...     \n",
       "2   110002    group_3  0  0.333333  0.565979    0    0    0    0    0  ...     \n",
       "3   110003    group_3  0  0.208333  0.316209    0    0    0    0    1  ...     \n",
       "4   110004    group_3  0  0.208333  0.008061  -99  -99  -99  -99  -99  ...     \n",
       "\n",
       "   x_148  x_149  x_150  x_151  x_152  x_153  x_154  x_155  x_156  x_157  \n",
       "0      1      1      1      1      1      1      1      1      3    -99  \n",
       "1      1      1      1      1      1      1      1      1      2      2  \n",
       "2      1      1      2      1      1      1      1      1      2      2  \n",
       "3      2      1      1      1      1      1      1      1      2      4  \n",
       "4      1      1      1      1      1      1      1      1      2      1  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_drop_col_row.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n",
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      "120\n"
     ]
    }
   ],
   "source": [
    "#缺失值统计\n",
    "column = ['x_1', 'x_2', 'x_3', 'x_4', 'x_5',\n",
    "       'x_6', 'x_7', 'x_8', 'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14',\n",
    "       'x_15', 'x_16', 'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22',\n",
    "       'x_23', 'x_24', 'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30',\n",
    "       'x_31', 'x_32', 'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38',\n",
    "       'x_39', 'x_40', 'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46',\n",
    "       'x_47', 'x_48', 'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54',\n",
    "       'x_55', 'x_56', 'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62',\n",
    "       'x_63', 'x_64', 'x_65', 'x_66', 'x_67', 'x_68', 'x_69', 'x_70',\n",
    "       'x_71', 'x_72', 'x_73', 'x_74', 'x_75', 'x_76', 'x_77', 'x_78',\n",
    "       'x_79', 'x_80', 'x_81', 'x_82', 'x_83', 'x_84', 'x_85', 'x_86',\n",
    "       'x_87', 'x_88', 'x_89', 'x_90', 'x_91', 'x_93', 'x_95', 'x_96',\n",
    "       'x_97', 'x_98', 'x_99', 'x_100', 'x_101', 'x_139', 'x_140',\n",
    "       'x_141', 'x_142', 'x_143', 'x_144', 'x_145', 'x_146', 'x_147',\n",
    "       'x_148', 'x_149', 'x_150', 'x_151', 'x_152', 'x_153', 'x_154',\n",
    "       'x_155', 'x_156', 'x_157']\n",
    "number1 = []\n",
    "count = 0\n",
    "for i in range(3,121):\n",
    "    print(i)\n",
    "    for j in range(13602):\n",
    "        if train_drop_col_row.iloc[j,i] == -99:\n",
    "            count = count + 1\n",
    "    number1.append(count)\n",
    "    count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c45abee940>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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t2L7yOPKH5OpBSeQTkbGTiltYcyRd7cne4WV7PJ88Cv2j5KsB/pm6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\n",
      "text/plain": [
       "<Figure size 2520x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nu1 = pd.DataFrame(number1)\n",
    "nu1.plot.bar(figsize=(35,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215\n",
      "215\n",
      "215\n",
      "215\n",
      "215\n",
      "215\n",
      "215\n",
      "215\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "213\n",
      "214\n",
      "5\n",
      "204\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "90\n",
      "586\n"
     ]
    }
   ],
   "source": [
    "miss_all_test = []\n",
    "for i in range(39,92):\n",
    "    col = 'x'+'_'+str(i)\n",
    "    miss = test_drop_col[test_drop_col[col]== -99].index.values\n",
    "    print(len(miss))\n",
    "    miss_all_test.append(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213\n",
      "213\n",
      "213\n",
      "213\n"
     ]
    }
   ],
   "source": [
    "miss_all_test1 = []\n",
    "for i in range(97,101):\n",
    "    col = 'x'+'_'+str(i)\n",
    "    miss = test_drop_col[test_drop_col[col]== -99].index.values\n",
    "    print(len(miss))\n",
    "    miss_all_test1.append(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "29\n",
      "1031\n",
      "90\n",
      "90\n"
     ]
    }
   ],
   "source": [
    "miss_all_test2 = []\n",
    "for i in range(143,156):\n",
    "    col = 'x'+'_'+str(i)\n",
    "    miss = test_drop_col[test_drop_col[col]== -99].index.values\n",
    "    print(len(miss))\n",
    "    miss_all_test2.append(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con = miss_all_test[0]\n",
    "count = 0\n",
    "for i in range(1,53):\n",
    "    con = np.concatenate((con,miss_all_test[i]),axis = 0)    \n",
    "drop_row_test = np.unique(con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con1 = miss_all_test1[0]\n",
    "count = 0\n",
    "for i in range(1,4):\n",
    "    con1 = np.concatenate((con1,miss_all_test1[i]),axis = 0)    \n",
    "drop_row_test1 = np.unique(con1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "con2 = miss_all_test2[0]\n",
    "count = 0\n",
    "for i in range(1,13):\n",
    "    con2 = np.concatenate((con2,miss_all_test2[i]),axis = 0)    \n",
    "drop_row_test2 = np.unique(con2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(997,)\n",
      "(213,)\n",
      "(1143,)\n"
     ]
    }
   ],
   "source": [
    "print(drop_row_test.shape)\n",
    "print(drop_row_test1.shape)\n",
    "print(drop_row_test2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1878,)\n"
     ]
    }
   ],
   "source": [
    "drop_row_con_test = np.concatenate((drop_row_test,drop_row_test1,drop_row_test2),axis = 0) \n",
    "drop_row_all_test = np.unique(drop_row_con_test)\n",
    "\n",
    "print(drop_row_all_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 120)\n",
      "(8122, 120)\n"
     ]
    }
   ],
   "source": [
    "test_drop_col_row = test_drop_col.drop(drop_row_all_test)\n",
    "print(test_drop_col.shape)\n",
    "print(test_drop_col_row.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>cust_id</th>\n",
       "      <th>cust_group</th>\n",
       "      <th>x_1</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.657454</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100002</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.604167</td>\n",
       "      <td>0.825764</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100003</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.473441</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100004</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.344635</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cust_id cust_group       x_1       x_2  x_3  x_4  x_5  x_6  x_7  x_8  \\\n",
       "0   100000    group_3  0.125000  0.659675    0    1    1    0    3    3   \n",
       "1   100001    group_3  0.250000  0.657454    0    0    0    0    0    0   \n",
       "2   100002    group_3  0.604167  0.825764    0    0    0    0    3    3   \n",
       "3   100003    group_3  0.312500  0.473441    0    0    0    0    0    0   \n",
       "4   100004    group_3  0.458333  0.344635  -99  -99  -99  -99  -99  -99   \n",
       "\n",
       "   ...    x_148  x_149  x_150  x_151  x_152  x_153  x_154  x_155  x_156  x_157  \n",
       "0  ...        1      1      1      1      1      1      1      1      2      2  \n",
       "1  ...        1      1      4      1      1      1      1      1      2    -99  \n",
       "2  ...        1      1      1      1      1      1      1      1      2    -99  \n",
       "3  ...        1      1      1      1      1      1      2      2      2      2  \n",
       "4  ...        1      1      2      1      1      1      1      1      2      1  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_drop_col_row.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
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      "117\n",
      "118\n",
      "119\n"
     ]
    }
   ],
   "source": [
    "#缺失值统计\n",
    "column = ['x_1', 'x_2', 'x_3', 'x_4', 'x_5',\n",
    "       'x_6', 'x_7', 'x_8', 'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14',\n",
    "       'x_15', 'x_16', 'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22',\n",
    "       'x_23', 'x_24', 'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30',\n",
    "       'x_31', 'x_32', 'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38',\n",
    "       'x_39', 'x_40', 'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46',\n",
    "       'x_47', 'x_48', 'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54',\n",
    "       'x_55', 'x_56', 'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62',\n",
    "       'x_63', 'x_64', 'x_65', 'x_66', 'x_67', 'x_68', 'x_69', 'x_70',\n",
    "       'x_71', 'x_72', 'x_73', 'x_74', 'x_75', 'x_76', 'x_77', 'x_78',\n",
    "       'x_79', 'x_80', 'x_81', 'x_82', 'x_83', 'x_84', 'x_85', 'x_86',\n",
    "       'x_87', 'x_88', 'x_89', 'x_90', 'x_91', 'x_93', 'x_95', 'x_96',\n",
    "       'x_97', 'x_98', 'x_99', 'x_100', 'x_101', 'x_139', 'x_140',\n",
    "       'x_141', 'x_142', 'x_143', 'x_144', 'x_145', 'x_146', 'x_147',\n",
    "       'x_148', 'x_149', 'x_150', 'x_151', 'x_152', 'x_153', 'x_154',\n",
    "       'x_155', 'x_156', 'x_157']\n",
    "number2 = []\n",
    "count = 0\n",
    "for i in range(2,120):\n",
    "    print(i)\n",
    "    for j in range(8122):\n",
    "        if train_drop_col_row.iloc[j,i] == -99:\n",
    "            count = count + 1\n",
    "    number2.append(count)\n",
    "    count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c45ac05470>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
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UsavThzRIwyC+RgMT3P3+4fIhDau3BjNbF3gz8Zt1nvA9\nyib6kIb+0GBmU9z9niq/V7cPaZCGNj7eCODuD1t4E25vwmfaFgyVD2nor3TUpGFbYGtgiSd+A7lO\n+37RUIcPaXjV/krgauDbrbrfwje+ZwHvdfe9O9jf7e5vqbqvTh+5aajRR246+iUvczW8DFwPbQca\n7+ruHQfV5fpogoY6fPSLhkH9uzrThRBCCCGEEEIIIYQQQgghRA8wsw2AE4ADgNfHzY8RZgOZ4+7l\nWTrL9sPekZ2bhhp95KajX/IyV8MS4CB3v7fNvgfdfWKChiwfTdBQh49+0TAY+ma6EEIIIYQQQggh\nhBBCCCGE6Anu/pS7H+/uW3n4RvY4d9/a3Y8HDkxw8RFgPHC9mT1pZk8C1wHjgA8nysjyUUMaavGR\nm44a7LN9NCQfPs/AfaRHJ2rI9dEEDXX46BcNA6I304UQQgghhBBCCCGEEEIIIcSQY2YPuPumGfaH\nufvcTA1ZPnLTUKOP3HT0S172Qz4Mu4Y6fPSNBnWmCyGEEEIIIYQQQgghhBBCiF5gZosG2gVMcfe1\nMnwPSUd2HWnoZT5E/7kDE1aZvMzV0Ev7ftFQh49+0TAqx1gIIYQQQgghhBBCCCGEEEKIQZgA/ClQ\n/ha2ATd2Mu7Q+TohRUANPrLSUJeP3HT0S172Qz40QUMdPvpFw2CoM10IIYQQQgghhBBCCCGEEEL0\nih8DY9z99vIOM7suwb4JHdm5aajLR246+iUv+yEfmqChDh/9omFA1JkuhBBCCCGEEEIIIYQQQggh\neoK7f2KQfTMTXAx7R3YNaajFB/l50Rd5mauhBvt+0VCHj37RMCD6ZroQQgghhBBCCCGEEEIIIYQQ\nQghRYsRwCxBCCCGEEEIIIYQQQgghhBBCCCGahjrThRBCCCGEEEIIIYQQQgghhBBCiBLqTBdCCCGE\nEEIIIYQQQgghhBBCCCFKqDNdCCGEEEIIIYQQQgghhBBCCCGEKPH/bUzysZDYNXcAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<Figure size 2520x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nu2 = pd.DataFrame(number2)\n",
    "nu2.plot.bar(figsize=(35,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_drop_col_row.to_csv('../data/train_drop_col_row.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_drop_col_row.to_csv('../data/train_x_drop_col_row.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14309\n",
      "691\n",
      "(14309, 160)\n",
      "(691, 160)\n"
     ]
    }
   ],
   "source": [
    "idx_0 = train[train.y == 0].index\n",
    "idx_1 = train[train.y == 1].index\n",
    "nb_0 = len(train.loc[idx_0])\n",
    "nb_1 = len(train.loc[idx_1])\n",
    "print(nb_0)\n",
    "print(nb_1)\n",
    "idx_list_0 = list(idx_0)\n",
    "idx_list_1 = list(idx_1)\n",
    "train_x0 = train.loc[idx_list_0]\n",
    "train_x1 = train.loc[idx_list_1]\n",
    "print(train_x0.shape)\n",
    "print(train_x1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2871</th>\n",
       "      <td>112871</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.650262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3107</th>\n",
       "      <td>113107</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.477204</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>113386</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.442955</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3960</th>\n",
       "      <td>113960</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.868474</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4121</th>\n",
       "      <td>114121</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.564521</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>114226</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>0.701994</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4249</th>\n",
       "      <td>114249</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.978194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4318</th>\n",
       "      <td>114318</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.893089</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>114362</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.598911</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4382</th>\n",
       "      <td>114382</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.741821</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4911</th>\n",
       "      <td>114911</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.782093</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4912</th>\n",
       "      <td>114912</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>0.904605</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4913</th>\n",
       "      <td>114913</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.702744</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4914</th>\n",
       "      <td>114914</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.564259</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4915</th>\n",
       "      <td>114915</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.679807</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4916</th>\n",
       "      <td>114916</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.593905</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4917</th>\n",
       "      <td>114917</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.397961</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4918</th>\n",
       "      <td>114918</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.487372</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4919</th>\n",
       "      <td>114919</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.722236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4920</th>\n",
       "      <td>114920</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.594372</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4921</th>\n",
       "      <td>114921</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.524059</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4922</th>\n",
       "      <td>114922</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.402191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4923</th>\n",
       "      <td>114923</td>\n",
       "      <td>group_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.388222</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>124970</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.467151</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>124971</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.815953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>124972</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.442484</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>124973</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.527271</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>124974</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.780648</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>124975</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.309055</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>124976</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.822654</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>124977</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.932908</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>124978</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.524847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>124979</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.430736</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>124980</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.448795</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>124981</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.305262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>124982</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.576172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>124983</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.471767</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>124984</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.529768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>124985</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.656954</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>124986</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.434173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>124987</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.178516</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>124988</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.815406</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>124989</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.895526</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>124990</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.362966</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>124991</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.695247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>124992</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.433978</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>124993</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.852851</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>124994</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.446379</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>124995</td>\n",
       "      <td>group_2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>124996</td>\n",
       "      <td>group_2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>124997</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>124998</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.659188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14999</th>\n",
       "      <td>124999</td>\n",
       "      <td>group_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.485906</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>691 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cust_id cust_group  y       x_1       x_2  x_3  x_4  x_5  x_6  x_7  \\\n",
       "46      110046    group_3  1  0.437500  0.743508    0    0    0    0    0   \n",
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       "2871    112871    group_3  1  0.270833  0.650262    0    0    0    0    6   \n",
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       "3386    113386    group_3  1  0.083333  0.442955    0    0    0    0    3   \n",
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       "4923    114923    group_3  1  0.187500  0.388222    0    1    1    0    4   \n",
       "...        ...        ... ..       ...       ...  ...  ...  ...  ...  ...   \n",
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       "14985   124985    group_2  1  0.125000  0.656954    0    0    0    0    4   \n",
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       "14989   124989    group_2  1  0.166667  0.895526  -99  -99  -99  -99  -99   \n",
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       "14999   124999    group_2  1  0.145833  0.485906    0    0    0    0    2   \n",
       "\n",
       "       ...    x_148  x_149  x_150  x_151  x_152  x_153  x_154  x_155  x_156  \\\n",
       "46     ...        1      1      1      1      1      1      1      1      2   \n",
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       "4923   ...        1      1      1      1      1      1      1      1      3   \n",
       "...    ...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "14970  ...        1      1      1      1      1      1      1      2      3   \n",
       "14971  ...        1      1      1      1      1      1      1      1      3   \n",
       "14972  ...        1      1      1      1      1      1      1      1      3   \n",
       "14973  ...        1      1      1      1      1      1      1      1      1   \n",
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       "14975  ...        1      1      1      1      1      1      1      1      2   \n",
       "14976  ...        1      1      1      1      1      1      1      1      2   \n",
       "14977  ...        1      1      1      1      1      1      1      1      1   \n",
       "14978  ...        1      1      1      1      1      1      2      2      2   \n",
       "14979  ...        1      1      1      1      1      1      1      1      2   \n",
       "14980  ...        1      1      2      1      1      1      1      1      2   \n",
       "14981  ...        1      1      1      1      1      1      1      1      2   \n",
       "14982  ...        1      1      1      1      1      1      2      2      3   \n",
       "14983  ...        1      1      1      1      1      1      1      1      1   \n",
       "14984  ...        1      1      1      1      1      1      1      1      1   \n",
       "14985  ...        1      1      1      1      1      1      1      1      2   \n",
       "14986  ...        1      1      1      1      1      1      1      1      2   \n",
       "14987  ...        1      1      1      1      1      1      2      2      3   \n",
       "14988  ...        1      1      1      1      1      1      1      1      2   \n",
       "14989  ...        1      1      1      1      1      1      1      1      2   \n",
       "14990  ...        1      1      1      1      1      1      1      1      2   \n",
       "14991  ...        1      1      1      1      1      1      1      1      2   \n",
       "14992  ...        1      1      1      1      1      1      2      2      1   \n",
       "14993  ...        1      1      1      1      1      1      1      1      3   \n",
       "14994  ...        1      1      1      1      1      1      1      1      1   \n",
       "14995  ...        1      1      1      1      1      1      1      1      2   \n",
       "14996  ...        1      1      1      1      1      1      1      1      2   \n",
       "14997  ...        1      1      1      1      1      1      1      1      3   \n",
       "14998  ...        1      1      1      1      1      1      1      1      2   \n",
       "14999  ...        1      1      1      1      1      1      1      1      2   \n",
       "\n",
       "       x_157  \n",
       "46         4  \n",
       "505        2  \n",
       "938        2  \n",
       "1401       4  \n",
       "1889       3  \n",
       "2152       3  \n",
       "2191     -99  \n",
       "2871       3  \n",
       "3107      10  \n",
       "3386     -99  \n",
       "3960       3  \n",
       "4121       3  \n",
       "4226       2  \n",
       "4249       4  \n",
       "4318       4  \n",
       "4362       4  \n",
       "4382       4  \n",
       "4911       3  \n",
       "4912     -99  \n",
       "4913       3  \n",
       "4914       3  \n",
       "4915       2  \n",
       "4916     -99  \n",
       "4917       3  \n",
       "4918       3  \n",
       "4919       2  \n",
       "4920     -99  \n",
       "4921       2  \n",
       "4922       3  \n",
       "4923       1  \n",
       "...      ...  \n",
       "14970      2  \n",
       "14971      2  \n",
       "14972      3  \n",
       "14973      3  \n",
       "14974      2  \n",
       "14975    -99  \n",
       "14976      3  \n",
       "14977      3  \n",
       "14978      4  \n",
       "14979      3  \n",
       "14980      4  \n",
       "14981      3  \n",
       "14982      2  \n",
       "14983     10  \n",
       "14984      3  \n",
       "14985      1  \n",
       "14986      3  \n",
       "14987      3  \n",
       "14988    -99  \n",
       "14989      1  \n",
       "14990    -99  \n",
       "14991      3  \n",
       "14992     10  \n",
       "14993      2  \n",
       "14994      4  \n",
       "14995      1  \n",
       "14996      4  \n",
       "14997      2  \n",
       "14998      2  \n",
       "14999      4  \n",
       "\n",
       "[691 rows x 160 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   },
   "outputs": [
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       "      <th>x_151</th>\n",
       "      <th>x_152</th>\n",
       "      <th>x_153</th>\n",
       "      <th>x_154</th>\n",
       "      <th>x_155</th>\n",
       "      <th>x_156</th>\n",
       "      <th>x_157</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110000</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.604988</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110001</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.012058</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110002</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.565979</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110003</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.316209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110004</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.008061</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110005</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.609981</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110006</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.531213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110007</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.781657</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110008</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.398122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>110009</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.489279</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110010</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.406162</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>110011</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.773108</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>110012</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.618428</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>110013</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.768463</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>110014</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.612821</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>110015</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.599402</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>110016</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.787073</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>110017</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.767217</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>110018</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.691687</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>110019</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>0.937100</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>110020</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.812465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>110021</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.606705</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>110022</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.602657</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>110023</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.474386</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>110024</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.701015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>110025</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.658653</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>110026</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.910505</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>110027</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.304457</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>110028</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.574380</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>110029</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.574897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14850</th>\n",
       "      <td>124850</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.724898</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14851</th>\n",
       "      <td>124851</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.689122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14852</th>\n",
       "      <td>124852</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.734663</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14853</th>\n",
       "      <td>124853</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.342050</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14854</th>\n",
       "      <td>124854</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.557524</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>124855</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.827057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14856</th>\n",
       "      <td>124856</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.522177</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14857</th>\n",
       "      <td>124857</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.131026</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14858</th>\n",
       "      <td>124858</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.604781</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>124859</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.598932</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>124860</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.956240</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14861</th>\n",
       "      <td>124861</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.508979</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14862</th>\n",
       "      <td>124862</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.469669</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14863</th>\n",
       "      <td>124863</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.762868</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14864</th>\n",
       "      <td>124864</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.485749</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14865</th>\n",
       "      <td>124865</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.742143</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14866</th>\n",
       "      <td>124866</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.689339</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14867</th>\n",
       "      <td>124867</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.576003</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14868</th>\n",
       "      <td>124868</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.402306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14869</th>\n",
       "      <td>124869</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.864333</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14870</th>\n",
       "      <td>124870</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.572171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14871</th>\n",
       "      <td>124871</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.523597</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14872</th>\n",
       "      <td>124872</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.520609</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14873</th>\n",
       "      <td>124873</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.910869</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14874</th>\n",
       "      <td>124874</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.553540</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14875</th>\n",
       "      <td>124875</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.470152</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14876</th>\n",
       "      <td>124876</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.602666</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14877</th>\n",
       "      <td>124877</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.319714</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14878</th>\n",
       "      <td>124878</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.393821</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14879</th>\n",
       "      <td>124879</td>\n",
       "      <td>group_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.486279</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14309 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cust_id cust_group  y       x_1       x_2  x_3  x_4  x_5  x_6  x_7  \\\n",
       "0       110000    group_3  0  0.354167  0.604988  -99  -99  -99  -99  -99   \n",
       "1       110001    group_3  0  0.125000  0.012058  -99  -99  -99  -99  -99   \n",
       "2       110002    group_3  0  0.333333  0.565979    0    0    0    0    0   \n",
       "3       110003    group_3  0  0.208333  0.316209    0    0    0    0    1   \n",
       "4       110004    group_3  0  0.208333  0.008061  -99  -99  -99  -99  -99   \n",
       "5       110005    group_3  0  0.250000  0.609981  -99  -99  -99  -99  -99   \n",
       "6       110006    group_3  0  0.208333  0.531213    0    0    0    0    3   \n",
       "7       110007    group_3  0  0.395833  0.781657  -99  -99  -99  -99  -99   \n",
       "8       110008    group_3  0  0.208333  0.398122    0    0    0    0    2   \n",
       "9       110009    group_3  0  0.270833  0.489279  -99  -99  -99  -99  -99   \n",
       "10      110010    group_3  0  0.291667  0.406162    0    0    0    0    0   \n",
       "11      110011    group_3  0  0.270833  0.773108    1    1    1    0    0   \n",
       "12      110012    group_3  0  0.125000  0.618428  -99  -99  -99  -99  -99   \n",
       "13      110013    group_3  0  0.479167  0.768463  -99  -99  -99  -99  -99   \n",
       "14      110014    group_3  0  0.354167  0.612821    0    0    0    0    2   \n",
       "15      110015    group_3  0  0.270833  0.599402  -99  -99  -99  -99  -99   \n",
       "16      110016    group_3  0  0.562500  0.787073    0    0    0    0    3   \n",
       "17      110017    group_3  0  0.166667  0.767217  -99  -99  -99  -99  -99   \n",
       "18      110018    group_3  0  0.208333  0.691687  -99  -99  -99  -99  -99   \n",
       "19      110019    group_3  0  0.520833  0.937100  -99  -99  -99  -99  -99   \n",
       "20      110020    group_3  0  0.229167  0.812465    0    0    0    0    0   \n",
       "21      110021    group_3  0  0.125000  0.606705  -99  -99  -99  -99  -99   \n",
       "22      110022    group_3  0  0.208333  0.602657    0    0    0    0    1   \n",
       "23      110023    group_3  0  0.354167  0.474386    0    0    0    0    0   \n",
       "24      110024    group_3  0  0.270833  0.701015    0    0    0    0    3   \n",
       "25      110025    group_3  0  0.645833  0.658653  -99  -99  -99  -99  -99   \n",
       "26      110026    group_3  0  0.229167  0.910505    1    1    1    0    0   \n",
       "27      110027    group_3  0  0.270833  0.304457    0    1    1    0    0   \n",
       "28      110028    group_3  0  0.104167  0.574380    0    0    0    0    0   \n",
       "29      110029    group_3  0  0.458333  0.574897    0    0    0    0    1   \n",
       "...        ...        ... ..       ...       ...  ...  ...  ...  ...  ...   \n",
       "14850   124850    group_2  0  0.187500  0.724898  -99  -99  -99  -99  -99   \n",
       "14851   124851    group_2  0  0.208333  0.689122    0    0    0    0    0   \n",
       "14852   124852    group_2  0  0.187500  0.734663    0    0    0    0    0   \n",
       "14853   124853    group_2  0  0.145833  0.342050  -99  -99  -99  -99  -99   \n",
       "14854   124854    group_2  0  0.166667  0.557524    0    0    0    0    1   \n",
       "14855   124855    group_2  0  0.145833  0.827057    0    0    0    0    0   \n",
       "14856   124856    group_2  0  0.104167  0.522177  -99  -99  -99  -99  -99   \n",
       "14857   124857    group_2  0  0.291667  0.131026    0    0    0    0    0   \n",
       "14858   124858    group_2  0  0.125000  0.604781  -99  -99  -99  -99  -99   \n",
       "14859   124859    group_2  0  0.145833  0.598932    0    0    0    0    0   \n",
       "14860   124860    group_2  0  0.291667  0.956240  -99  -99  -99  -99  -99   \n",
       "14861   124861    group_2  0  0.125000  0.508979    0    0    0    0    0   \n",
       "14862   124862    group_2  0  0.208333  0.469669  -99  -99  -99  -99  -99   \n",
       "14863   124863    group_2  0  0.125000  0.762868  -99  -99  -99  -99  -99   \n",
       "14864   124864    group_2  0  0.145833  0.485749  -99  -99  -99  -99  -99   \n",
       "14865   124865    group_2  0  0.104167  0.742143  -99  -99  -99  -99  -99   \n",
       "14866   124866    group_2  0  0.270833  0.689339  -99  -99  -99  -99  -99   \n",
       "14867   124867    group_2  0  0.125000  0.576003  -99  -99  -99  -99  -99   \n",
       "14868   124868    group_2  0  0.145833  0.402306    0    0    0    0    0   \n",
       "14869   124869    group_2  0  0.291667  0.864333  -99  -99  -99  -99  -99   \n",
       "14870   124870    group_2  0  0.125000  0.572171    0    0    0    0    0   \n",
       "14871   124871    group_2  0  0.229167  0.523597  -99  -99  -99  -99  -99   \n",
       "14872   124872    group_2  0  0.250000  0.520609  -99  -99  -99  -99  -99   \n",
       "14873   124873    group_2  0  0.208333  0.910869  -99  -99  -99  -99  -99   \n",
       "14874   124874    group_2  0  0.187500  0.553540  -99  -99  -99  -99  -99   \n",
       "14875   124875    group_2  0  0.125000  0.470152  -99  -99  -99  -99  -99   \n",
       "14876   124876    group_2  0  0.166667  0.602666    0    0    0    0    4   \n",
       "14877   124877    group_2  0  0.145833  0.319714  -99  -99  -99  -99  -99   \n",
       "14878   124878    group_2  0  0.208333  0.393821    0    0    0    0    1   \n",
       "14879   124879    group_2  0  0.229167  0.486279  -99  -99  -99  -99  -99   \n",
       "\n",
       "       ...    x_148  x_149  x_150  x_151  x_152  x_153  x_154  x_155  x_156  \\\n",
       "0      ...        1      1      1      1      1      1      1      1      3   \n",
       "1      ...        1      1      1      1      1      1      1      1      2   \n",
       "2      ...        1      1      2      1      1      1      1      1      2   \n",
       "3      ...        2      1      1      1      1      1      1      1      2   \n",
       "4      ...        1      1      1      1      1      1      1      1      2   \n",
       "5      ...        1      1      1      1      1    -99      1      1      2   \n",
       "6      ...        1      1      1      1      1      1      1      1      2   \n",
       "7      ...        1      1      1      1      1      1      1      1      3   \n",
       "8      ...        1      1      1      1      1      1      1      1      2   \n",
       "9      ...        1      1      1      1      1      1      1      1      1   \n",
       "10     ...        1      1      1      1      1      1      2      2    -99   \n",
       "11     ...        1      1      1      1      1      1      1      1      2   \n",
       "12     ...        1      1      1      1      1      1      1      1      2   \n",
       "13     ...        1      1      2      1      1      1      1      1      2   \n",
       "14     ...        1      1      2      1      1      1      1      1      2   \n",
       "15     ...        1      1      1      1      1      1      1      1      2   \n",
       "16     ...        1      1      1      1      1      1      1      1      2   \n",
       "17     ...        1      1      1      1      1      1      1      1      2   \n",
       "18     ...        1      1      1      1      1      1      1      1      2   \n",
       "19     ...        1      1      1      1      1      1      1      1      2   \n",
       "20     ...        1      1      1      1      1      1      1      1      3   \n",
       "21     ...        1      1      1      1      1      1      1      1      2   \n",
       "22     ...        1      1      1      1      1      1      1      1      1   \n",
       "23     ...        1      3      2      1      1      1      1      1      2   \n",
       "24     ...        1      1      1      1      1      1      1      1      2   \n",
       "25     ...        1      1      1      1      1      1      1      1      2   \n",
       "26     ...        1      1      2      1      1      1      1      1      2   \n",
       "27     ...        1      1      1      1      1      1      1      1      3   \n",
       "28     ...        1      1      1      1      1      1      2      2      2   \n",
       "29     ...        1      1      1      1      1      1      1      1      2   \n",
       "...    ...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "14850  ...        1      1      1      1      1      1      1      1      2   \n",
       "14851  ...        1      1      1      1      1      1      1      1      2   \n",
       "14852  ...        1      1      1      1      1      1      1      1      2   \n",
       "14853  ...        1      1      1      1      1      1      1      1      2   \n",
       "14854  ...        1      1      1      1      1      1      1      1      1   \n",
       "14855  ...        1      1      1      1      1      1      1      1      2   \n",
       "14856  ...        1      1      1      1      1      1      2      2      1   \n",
       "14857  ...        1      1      1      1      1      1      1      1      2   \n",
       "14858  ...        1      1      1      1      1      1      1      1      2   \n",
       "14859  ...        1      3      2      1      1      1      1      1      2   \n",
       "14860  ...        1      1      1      1      1      1      1      1      2   \n",
       "14861  ...        1      1      1      1      1      1      1      1      2   \n",
       "14862  ...        1      1      1      1      1      1      1      1      3   \n",
       "14863  ...        1      1      1      1      1      1      2      2      1   \n",
       "14864  ...        1      1      1      1      1      1      1      1      2   \n",
       "14865  ...        1      1      1      1      1      1      1      1      2   \n",
       "14866  ...        1      1      2      1      1      1      1      1      2   \n",
       "14867  ...        1      1      1      1      1      1      1      1      2   \n",
       "14868  ...        1      1      1      1      1      1      1      1      2   \n",
       "14869  ...        1      1      2      1      1      1      1      1      2   \n",
       "14870  ...        1      1      1      1      1      1      1      1      2   \n",
       "14871  ...        1      1      1      1      1      1      1      1      1   \n",
       "14872  ...        1      1      1      1      1      1      1      1      2   \n",
       "14873  ...        1      1      1      1      1      1      1      1      3   \n",
       "14874  ...        1      1      1      1      1      1      1      1      3   \n",
       "14875  ...        1      1      1      1      1      1      1      1      2   \n",
       "14876  ...        1      1      1      1      1      1      2      2      2   \n",
       "14877  ...        1      1      1      1      1      1      1      1      2   \n",
       "14878  ...        1      1      1      1      1      1      1      1      1   \n",
       "14879  ...        1      1      1      1      1      1      2      2      2   \n",
       "\n",
       "       x_157  \n",
       "0        -99  \n",
       "1          2  \n",
       "2          2  \n",
       "3          4  \n",
       "4          1  \n",
       "5        -99  \n",
       "6          4  \n",
       "7          2  \n",
       "8          3  \n",
       "9        -99  \n",
       "10       -99  \n",
       "11         4  \n",
       "12         3  \n",
       "13         3  \n",
       "14         1  \n",
       "15         4  \n",
       "16         2  \n",
       "17         2  \n",
       "18         4  \n",
       "19         4  \n",
       "20       -99  \n",
       "21       -99  \n",
       "22       -99  \n",
       "23         4  \n",
       "24         4  \n",
       "25       -99  \n",
       "26         2  \n",
       "27         1  \n",
       "28         3  \n",
       "29       -99  \n",
       "...      ...  \n",
       "14850      1  \n",
       "14851      3  \n",
       "14852      2  \n",
       "14853      2  \n",
       "14854      3  \n",
       "14855      4  \n",
       "14856     10  \n",
       "14857      3  \n",
       "14858      2  \n",
       "14859      2  \n",
       "14860      2  \n",
       "14861      2  \n",
       "14862      1  \n",
       "14863     10  \n",
       "14864      3  \n",
       "14865      3  \n",
       "14866      3  \n",
       "14867      2  \n",
       "14868      2  \n",
       "14869      2  \n",
       "14870      3  \n",
       "14871      4  \n",
       "14872      1  \n",
       "14873      2  \n",
       "14874      2  \n",
       "14875      2  \n",
       "14876      4  \n",
       "14877      3  \n",
       "14878      4  \n",
       "14879      3  \n",
       "\n",
       "[14309 rows x 160 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2133892eeb8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RJo6GZBstC1wdNBZTB8xsGOT+AZv3hH8p8YtIFdkQeCEPSy8HhRfNbGDWEYmSf80zs73A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\ng5MjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2133892e438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x1['cust_group'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213389224e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SqVePA/sX4fMpUDzI3e+NHZG0Lhc7AKkOZnYgxissTj+OqsMEAGBp4BCg23SwtQh7EYtI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x213389b9828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x0['cust_group'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "count = []\n",
    "number = 0\n",
    "for i in range(691):\n",
    "    for j in range(160):\n",
    "        if train_x1.iloc[i,j] == -99:\n",
    "            number = number + 1\n",
    "    count.append(number)\n",
    "    number = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "aa = pd.DataFrame(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "aa.columns = ['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "count1 = []\n",
    "number = 0\n",
    "for i in range(14309):\n",
    "    for j in range(160):\n",
    "        if train_x0.iloc[i,j] == -99:\n",
    "            number = number + 1\n",
    "    count1.append(number)\n",
    "    number = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bb = pd.DataFrame(count1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bb.columns = ['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338aa3be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j0M57rwPhxuCWoUu6RmI4DBgbgOhA4NYkXPAi4BPn3CoA51wYFf6D0LqKW9CE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\nse+99+7aqeply1RPnz4dVq7claRhKwOece+BYrdkGXeAbNRat1j0nY6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UC+bhaGB6gwaq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x213389f6828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bb['b'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338cf00b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jXx7a0BSWoWv3p0bVe77Q+R0dfVV1AfcjmkJp5Df/RpvDsbdzztJXG6FoRD7s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\nKtSOe7SG7lS7Prxa20vRiWJnqoU9NT6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P6+jR9Fy7ST6Gk+q3JhAwwWAgLFkTxOkI5yBU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\nf5aeyH27cd9OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338cf0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aa['a'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338d25f28>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r5Pu3At5I+3CjFsIEfH8ntM7H0fABv5b2nabltfpIdzczO4xwnnz1Hv3XCf0i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\nQf0/YFmdWUVA7IoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338d25400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x0['x_3'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338e0f550>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5oNu0Wcu+wErvheUgRerRJ2BDIJkcAn7p2PWIRDKOChlvxy6jmjq7AMDqhIkF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\nCnkREZGC+n/yE73y3Xlb8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338d1e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x1['x_3'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338e8a4e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IjsU0d06YBtwHudcgX4ihxCcBJxInIcuhzQMEOtGD0NAQvQWVcoDwwAPwwx/y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338e983c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x0['x_4'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338ee9160>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KWAN4HZgNnEDsgO1CTFv+APgGEf4LMxO4kjiO39EwYHSH50YTod2ZSURXsj8QOw2jgFWA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\nJfIA0JTIA0BTIg8ATYk8ADT1AR0xZNM8XH0nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338ef96a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x1['x_4'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338f5def0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIcAkoKGb6pLqsxXwvxx8dmWY+F8z28fd70tdVU+hlnyNM7MjwW6FvXrBOAV8XVueaDG/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\nkwtMhBwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338f72080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x0['x_5'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21338fbf940>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HwL+BN4hLE3YDVgR2c/f/luJ3LAuN5JfdnFZ3IhXvJeBEyPwHioVYjL0FcVVV\nzy6+5TKM8Osl8HPAgOTWFX8DvkG0Dh1PXPV+A/Ay8b/sdKIRyZWFgkFcIb8G0aBkQfJzvwP+Qfyv\nnU0MIa8DDicma6Yl369CJM98YJ478/J53geeKBbZOJ+ncf58FoC9DAxsbFy8hK//889TePFFL8Ss\nBgWwmB1fzhmNpgONptMjzQcCTs+e0KOH0atX5w4qlnSg0fb5Dz9sqqCUH+hDgCuJyS0j9nc5J42A\nhy6EvJnt6O73dPDcUe7+h+TbabS/gqTaKeSlChSB/wf8HGxafNRsCIwjrqAq5aBJgV8SXybWuf+A\naFszFriDCHiIJrZvtnj9IqI/3tvExXMbA3fTuqfdpUTnsj8D05P3Ogg4n+b1+k22Iw4qPtfisduA\nY5LfdTlwKECxuMS/PS1nNFrOZjTdmmY1Wp06cWd+Ps/8fJ4FcYDRdNrEmk6ftFmj4fnkQKO4vAca\n2Wyj5/Mlyyp3P9zM/g/4IrB92rPZyzxdb2YLgd8AZ7h7Y/LYSsCfgG3dvZSdgypOco4lDxdnovmE\nSCV5C/geZG6C4qIYVo4nEqO7WztoSl9SkicOKJrWZ8xu8X3TAcYc4FbgcXh/kfsqpfrdZvZ74EBi\nod2rLZ6a5e4LSvV7Ol1PF0J+a6JzwhzioHAN4oLaV4BD3H1aqYusNGYNs+H8FaNzoUgluBb4YTLB\nSuwpMo7411kJlzUr8KUCHQlcCc8udB9Tqvc0izWZ7Tx1qLtfVarf01nLPF3v7g+b2ViiHdOTxEfI\nWcDPvFyr+CpOZjq8uV7aVUi9mwmcAnYt+LzobTKO6Jjddi42bZrSlwr0AdAYZ0JKxt0r4bB6sa4u\nvFuXWJP7FrGeYxQxGVgn15U1ToFpo6jdhj9S0W4HzgR7MsYLaxBT8utQHT2aFPhSISZBo8dOPzVr\nmY84zOw0ogfvncRSnvHElknPmtlWpS2vUvlUmFKCa1dEOmsecCLYAIfPQa8nY0+S44hW5+tRHQHf\nVlPgnwEcBYX14e4e8E1iifIEYsHXzCW9h0gXODA5MvDVpb22mnXlnPw7wGHu/u8WjzUAPwaOc/eu\nXpRTNczsROj9M5ib0WBeymsi0bTmoWhasxrNTWtq+QJYncOXMnsbGB5fftHdb061mDLqSsiv5O4z\nOnhu+6XtVlcLzGwv4Ka4KGVY2uVIzWkEfkJc9fx+dKBralpTsjXAVaSDwP8KcY2SAl+64l5iz3Rg\ntLu/nGbtXEHfAAAdPklEQVQt5bTM0/UdBXzyXM0HfOLVVnciJfEC8DnI9AbOhiHvw+eJXT/3pD4D\nHjqc0j+M5in9P1F9U/pvEzuXrEQsaBpDrGRekkXETvAjiS54awJXtHnNLODbxPCjF3Em5/YWz19N\ndGkZTFxn39JUYoFVPXQBST69iyQ95mtVLU/4ldOUaF39UgZ2SLsWqWpFYsT+C+CNGKY2Na0Zjs4G\ntbWERXvVNML/mNitfWei4c1KxObVS2sysj+xIvxPwFrEJEfLzYAbgV2ILsV/p7nLXVMXvg+BI4hr\noNcA9khq2CN5/ttEH9YVuvwnqx6vAT1g+kL3RWnXUk4K+S5w90VmPZ6HiRvDt9IuR6rSG0Sr2ZvT\nb1pTrao48H9KjKb/2OKxEUv5mduJ/vZTaA7ttpsCXUYcQDxK8zrMlq9p+tkvJd/vSDQ93oPotNCD\n+O9VD56FYh6eT7uOcivbBjW1zsx+B2sdAZMalv5qkSZXE01rksnC9WhuWqNRe2ks4Rz+3ix9tNwd\nNgB2J9rU3kdM2hxD9JjvyLeJ0edmRJvavkRLtfOIaXmIszqDgd7ATTS3sT2VODf7MTHVfx+wOvFX\n7w/Eco9xyeP1sMqoCPSHwpzoKX9+2vWUk0byXfcITD4mzgRW2jhBKssMomnNXx2fb6xAfKJuQuU1\nrakFSxjhH0FMZzet0k8r8KcQOwucSJxjf4y4GrIncZ6+o595gAj0fxJ/q75FfAJd1uI1/wW+RmyD\nNil5TZ7oWDaA2DnlYGJTm28Q/z0OT37/ZGIZSB44G9ivJH/ayvMSMCeO/x5Ou5Zy00i+i8xsLWAS\n/IvmM1oiLd1GNK15Oi7KXYsI92ppWlNr2hnhpxX4PYmzMw+0eOx44HFiy7L2TAAeJDavaTpn/g/i\nPP3c5D1HERu7vE7zxNBFxIqP6R28733AKcRq87WJXeqGJPVNItYL1JpLgSOTAb271/Q6Q43ku24K\n5D6CRwYq5KXZHOAssD85PsvoRcyvboYmfNLWzgj/rklwRwoj/KHA6DaPjSYWyy3pZ4bTelHcaOL4\n8S3iGHIocV7d2rzmXWJ03vYDfxFxGuBqItALwLbJc+sSXRr27MwfqMo8DDTAC4tqPOChMrauqErR\np7/wEDxcXPqrpfY9DGwDmX7Ar2C1WcZ+xOVvu6KArzTtXJZ3V3JZ3srE+fI/AR+V6ddvQ+zo1dIr\nLHnx3TbEZXfz2vxMhuiR1PSaSe2871DaH9GdR2wtO4YI+JZtPBuTx2rR/dDYCPenXUd30HT9cogW\nv73Ph4+zcfws9WURzU1rPoimNZsQo/Z6vaa92nXTlP7jRCCfQ+wjP5HYuPpSYoEgwBnEFPuVyfdz\niUaHWyY/9wExA7EjsVsYxIh+Q6LT8bHEteDfBL4LnNamhheBfYGniIV6C4iV+D8l/vruT5yjH1qC\nP28leY+4xBD4qrtfk2ox3UAhvxyS3fieijb+u6RdjnSb54nL3+6GYiFOYG5BfLrWfFPnOlLmwL+N\nCN5JxMUVJxIzCU0OJa5x/2+Lx14lwvshYhX9AcRovOVfu4nACcDTxPT+4cQ597YXb2xHHEh8rk1N\nxxCHrz9Kaqg1fwIOi7McQ939vbTrKTeF/HIwM4OGN+HI4fB/aZcjZVUEfgv8EngzPvE3Iq49UtOa\n2vcOcD9kJ0fg52jdS78SLsuTzvki+G3wWKP7lmnX0h0U8svJzH4Nqx4Db+f0SV+LptHctKYxPs3H\nEycx1bSmPinwq9Y8YBAUF8Lp7v6ztOvpDgr55WRmOwF3x1m2zdIuR0rmKuBHzU1rRhOXv41Ex3LS\nTIFfVW4hGggB67l727WPNUkhv5xim93cDDi9H/ww7XJkucwATga7Dnw+rEhz05oV061MqoACv+Id\nDlwFkxe5r512Ld1FIV8CZvZnWP8r8IL6DlSlW4Hvgz0Ty3HWprlpjS4yla5Q4FecAjAE8jPhInc/\nJe16uotCvgTMbD/ghrgidd20y5FOmUN0o7vC8dnRtGZz4oyLPoGllDoI/KbNc/TXrXvcCewWX27p\n7hOX9/3MbDvgZOJTYyiwt7vfvLzvW2oK+RIws16QexdO6B8bNUrlehA4FTKPQNFjl47xxDl3zcNI\nub1NXJanwO92B4HfAJMbYV0vQfCZ2e7A1sATRLPCfRTyNczMLoSBx8I7OV0sXWmarvr9PTCjuWnN\n5sQ17iJpUOB3m1nAkNjU+Qx3v6DU729mRTSSr21mth7wUuzK/JWlvVy6xbPASUnTmmK08RpPXN+u\nBoVSSRT4ZfV74DvJ3J27v13q91fI1wmzhgdh6y3hPu0xlpoi8Buiac1bzU1rxhFNa0QqnQK/pBzY\nEPIvw60F933K8TsU8nXCzA4CroaXiU0fpftMA06AzK3RtGYQzU1reqdbmUiXKfCX2yPEiXNgd3e/\noxy/QyFfJ8ysZyzAO25AjCSl/K4kmta8Ft+qaY3UqnYCv6mXvgK/YweA/x3ezMMa7l6WXUMV8nXE\nzH4O/U6At7LqoFIu7xNNa/7m+AJT0xqpOwr8TnkNGAXucIy7X7zUH+gihXwdMbPVIPM6nJ+D09Mu\np8bcDJwF9qya1og0UeB36AjgCpiRjwV3C0r53mbWl/gUMuBJ4HvAPcBMd3+zlL9reSjky8DMfg8D\njoQ3s7BC2uVUudlEN7orwD+J8+uboaY1Iu1R4C82HRgJnodT3f3npX5/M9ueCPW2IXqlux/Wzo+k\nQiFfBmb2GbDJ8JMcnJp2OVXqfuBUsIkx2fYZYiHdeqhpjUhn1Hngfw/4DXxSgOHu/kna9aRFIV8m\nZnYxDPwmvJHTaL6zFgHngf0/8A/jWvampjUrp1uZSFWrs8CfAawOxQXwI3f/Qdr1pEkhXyZmNgJs\nElyQi/bG0rFnge9B5p5oWrMqMWrfEDWtESm16cCDtR34pwE/hwXFOBc/I+160qSQLyMzuwQGHRqj\n+b5pl1NhisBFyW16NK3ZmBi1q2mNSPeowcB/HVgXink4z93PSbuetCnky8jMRkLmNfhBDs5Ou5wK\n8Toxar81/hkOArYgAl5Na0TSUyOBn1wXPyO5Ln5u2vWkTSFfZmb2Y+hxKrycgTXSLiclRZqb1kyO\nC06amtaMQE1rRCpNlQZ+i+52h7r7FWnWUikU8mUW11LmJsGeQ+CfdXY19/vASWDXgy+IRjXjicV0\nWosoUh06CPym1roDUi2umQPjofA0PJ+HTcvV3a7aKOS7gZl9GbgO/g3snnY53eAmomnNc/Evbx1i\n1L42alojUs0qOPD/ChwYX+7o7vemWEpFUch3AzMzyN4DI7aBF2t0v/nZwOlgV4HPifPrmxNNayrl\nUF9ESqeCAn82MBry78JtBfcvduOvrngK+W5iZhtEP9afZGqrQc69wGmQeSx2ax6BMw5T0xqROpJy\n4B8DXALzC7C+u08t86+rKgr5bmRmF0Kv4+CVbLRwq1aLgHPBLgafGdeyb0qM2tW0RqS+dXPg3wvs\nGF9+x91/V+K3r3oK+W5kZv0h9xJsMwT+m62+E9RPAydC5t5oWjOUWEi3AWpaIyKfVubAnwdsAPm3\n4LE8bKfFdp+mkO9mZrYTcHfsN/+9tMvphCJR66+At+Nf6UbEQrphadYlIlWlncDflebL8roS+CcC\nv4JFRdjQ3V8rYbU1QyGfAjP7JeS+C09londrJZpMNK25LZrWDCZG7WOAXulWJiJVbjrRS39K1wP/\nUeKaeIeT3f0X5Su2uinkU2BmvSD3FKy3NjxeQavti8DlwE/ApkSTmvWJVfJqWiMi5dCFwJ8DbAL5\nqfBsHrZw93z3FVxdFPIpMbOxYP+Dk3NwQcrVvEs0rbkBfCH0I6bj1bRGRLpTJwP/G+B/gQUFGOvu\nr6ZUbVVQyKfIzE4F+2msD/1sChX8A/gB2PPNTWvGA2tRfWsCRaS2dBD4o4EL4xVfd/er0iuwOijk\nU2RmWcjeCytvCU/lYo/VcpsNnAb252ha04eYjt8UNa0RkcrUFPiToJAHgzuK7vXQPnS5KeRTZmbD\nIPc0jB8E92TLdy3af4HTIfO/5qY145OmNdky/UoRkVJZCFxCnpm8gbO1u7+XdknVQCFfAcxsK7D7\n4egc/L6E77yAaFpzSeumNZsDK5Xw14iIlJMDN+C8yAKcTd395bRLqhYK+QphZocDl8IlwBHL+W5P\nAidB5r7WTWs2BBqW861FRLrbRGJ/LzjA3f+WbjHVRSFfQczs95A7Gu6zxbsid1qeWI7yK+CdWKWy\nMbFKfmiJCxUR6S6vANfiwK/d/YS0y6k2CvkKYmY9YiHeoHHwdK5zLeVeI5rW3N7ctGYLIuDVtEZE\nqtl04E8UKXALzn7uXki7pGqjkK8wZrYK5J6BjVeC+7PQt51XFYE/Aj8Fe725ac04Yt8bNa0RkWo3\nE7iUPAt5iiI7uPu8tEuqRgr5CmRmm0L2Qdi1J9ycaT6R/jZwMmRuhGLStGY80bSmvWMBEZFqNI8I\n+Fm8RZHx7v5B2iVVK4V8hTKzXcFug4Oz8AWDc8BeiFWm6xKjdjWtEZFa0whcSYG3mZ0E/KS0S6pm\nCvkKZmYHAVcD0bRmHHEJXP8UixIRKZcicD3OyyzC2d7dJ6ZdUrXTOLCCufs1wGVALKbbEQW8iNQm\nB+4AXgKcLyvgS0MhX+Hc/XDgbO4hrhUVEak1DtxF02fcd9z95lTrqSEK+epwHvBL/g08lXYpIiIl\n1DSCfwiA77p7Kdt+1j2dk68SZmbAH4Aj+DzRmlakpf8BjwMfJ9+vDGxP7C4Isdnh88AsYr+CYcBO\nwGpLed9HkvedRawNWR/YhWi41GQ2MRJ7jVg4NZjYG7Sp1cNDwMPJ19vQutfTW8BtwOFo2FFvnOhk\n9xgA31bAl15u6S+RSuDubmZHAwu5le+wkPiwFGnSnwjfwcSH59PAX4GjicAfDOwBDCQaJD4C/Bk4\nngjv9jwL3A3sTRwMfAj8k+jFMCF5zXzgcmAN4ODkvT4EeifPv0ccYHw1qetqYG1gCLHQ6lZgLxTw\n9aZIHNw9DsBR7n5JqvXUKIV8FXH3opkdB8ziTs5kATESU/Mbgbi0sqWdiQ/Qt4iQ36jN8xOIbQ7e\nIwK6PW8RDZY2TL4fkHw9vcVrHiQOML7Y4rGW2xbPAFYBRibfr5I8NoQY4Y+kc80dpXbEwZ3zJADf\ndPc/pVtQ7VLIVxmP8yvfN7NZPMDPWAjsjkZB0loReIGYOm9vOr5AHAD0IkK3I6sTo/npwHCiC9lr\nwNgWr3mVGJn/DZgGrEhc7rlZ8vwQYmQ/ixjJz0wem0nMNhy1rH84qWpF4CacZwD4urv/Od2CapvO\nyVcxMzsSuJgxwF6Y9oUX3iMuuswTWwvvR/M5eYhAvoEI/xWBr7D0UfRE4D9EQDuxHmTPFs+fn9xv\nTZyvn06cZ/0CMCZ57nHi9IABWxEHAFcRHRsLwH3EOoHdgRGd/+NKlckTAf8cDnzN3a9Nu6Rap5Cv\ncmZ2IPBn1iPDlzDNzdS5AjFiXgi8CDwBHEpM10OE+ydE29AngSnEzsYdtUV+HbiRmPpvGsn/m2jK\ntH3ymvOS5w5r8XP/Jrowf7OD932a2F1sT+D/gCOTuv8OfBd0wFqD5gHXUeANHOcgd78+7ZLqgSZ5\nq1xyJLw3r9DI1RRZkHZFkqosMIjYXnhnYFVa91doSJ5fjebFbku6LPMeYkfDTYgp9vWS932wxWtW\nAFZq83MrEaHdnrnEyP1zxKh/cFLTGsRByodLqEeq04dEL/o3+QRnJwV891HI1wB3vxVnAlOZyyXk\n9SEpizkxRdrV5xv59KdE00LPpknAz/DpYP6Qjrsz3kFM2fcjzs8WWzzX9nupftOASygwizcoMs7d\nH0i7pHqikK8R7n4vzuZ8zDT+QAFt6VB/7iI+UD8mzs3fBUwlRuKLiEvh3kqef5u4FO4TYIMW7/GP\n5OeajCKuv38e+AiYTIzuR9Ec9lsm7/sAMZ3/LHEqYHw7NU5OXtP03HBipf1rxHn7DJ+eFZDq9Qxw\nJUUW8VAS8Ppk6mY6J19jzKw/xnU4uzEBY0t0iV29uIk4hz4H6Emsmt8WWJMYrd9ITI/PI65hHw58\nltYL764gLn/bO/m+SIT3M8QBQR8i4HciVuY3eZU4OJhJXIe/FXHevqVGop3T/rRe0f8k8F/iWp89\nab1QUKqTE70R7gPib9VR7r4ovYLql0K+BplZFvgxcApjcT6vBXki0k0aiRX0z2PAGcBPXUGTGoV8\nDTOzr2FczjAyfIUsK6ZdkYjUtJnA9RR4lwLO17TALn0K+RpnZuPJcAt9GMSB5BiedkUiUpNeAP5J\ngQLTKbKvuz+RdkmikK8LZjaMDDcDm7ArGbZASy5FpDQaiSsmHgeM63GOcPeOLqCUbqaQrxNm1pM4\nT/891qTIPmQ0fS8iy+VD4DryfIDjHAf8QeffK4tCvs6Y2W5kuJqeDGBvcoxKuyIRqUrPATdRoMgb\nyfT802mXJJ+mkK9DZjYE4wqczzEO2I3ohCYisjSNRNvi2EHuWuLyuE/SLEk6ppCvU2ZmwDEYFzKY\nDPuTW+JuZCIibwL/JM9MijjHAJdrer6yKeTrnJltSIa/AaPYjQzj0aI8EWltEdGw6FEgw1MUOdjd\nX0i5KukEhbxgZr2AC4DjWJ0Ce5FdvGuZiNS314nR+2yKON8HLnL3Je14IBVEIS+LmdlOZPgjMJJt\nMbZD5+pF6tUC4E5iu2LjIZzD3P3VlKuSZaSQl1aSUf0ZwOkMBL5AjjVTLkpEuterwM3kmUsjzknA\nxe6u/QGrkEJe2mVmo8nwR4pszRic3TD6pl2ViJTVXOB2nOcwjDuTxjbT0i5Luk4hLx0yswxwKMaF\n9KQvu5NlDNrVTqTWFIgthf9LgUbm4RwLXKWV89VPIS9LZWZDgIuAgxhBgT3I6nI7kRrgwGvA7eSZ\nSRa4FDjL3d9PtzApFYW8dJqZ7UqGP1BkJJsAO2L0S7sqEemS94HbKTKFDMZ9OMe7+zNplyWlpZCX\nZWJmDcBRZPghRn+2JsM2QK+0KxORTpkL3As8jmO8QZHjgZs1NV+bFPLSJWbWDzgF4yR6kmNHsmwO\nZNOuTETa1fq8+wKcc4DfuvvCdAuTclLIy3Ixs9WAc4FDGUiBXckxGi3OE6kUBeB54F7yfEQWuAT4\ngc671weFvJSEmW2E8XOcCQynwG5kGZF2VSJ1rHW45zD+hXOGuz+bdmnSfRTyUlJmtjMZLqTIxqxO\nge3JshYa2Yt0lwKxDex9i8P9Vpxz3P2JtEuT7qeQl5JLrq//PBl+QJHNWCUJ+/XQ5jci5dIU7veS\n52NyGLfgnKtwr28KeSmbZDvbnTC+j7MDg8jzWXJshBboiZTKp8P95iTcn0y7NEmfQl66hZltmYT9\nnvQjz7bk2ARtgCPSVQuBp4BHyDOLHMZNSbg/lXZpUjkU8tKtzGxjYgOcL9OHAluRY1NQX3yRTvoY\nmAg8ToFGAK4HLnD3p9MsSyqTQl5SYWbrAKdiHIKRZUOMcRiroUV6Im058AYwEeclAObg/B74P3d/\nK83SpLIp5CVVZrYScCgZjqXI6gwhzxbJefseaVcnkrJFxPn2ieR5nxwZXqfIL4Er3X1OytVJFVDI\nS0VIVuRPwPgOzufoQZFNki56K6ddnUg3+wB4EniCAovIYNyG8xvgLu3rLstCIS8Vx8xGEv3xj6LI\nQEZSYDxZ1gVy6dYmUjbziOY1T5HnHXIYs3EuAX7v7q+nXJ1UKYW8VCwz6wl8KZnK34JeFNiYLBsD\nw9G5e6l+BWAS8DTOKzhFwPg3zhXALeorL8tLIS9Vwcw2Ag4hw9cpsjIDyTOWHBsDA9OuTmQZvQM8\nAzxDnvnkyPAiRS4Drnb391KuTmqIQl6qipllgZ2AgzH2x+nFcApsRJYNgBVTLlCkIx8DLxLT8R+Q\nI8NHFLmSWESny9+kLBTyUrXMbAXgCxgH4ewO5BiRTOmPAlZIuUCpbw68D7wMvEie98hh5HFuBq4A\nbnf3xjRLlNqnkJeaYGYDgX2SwN8JMIaRZz1yrAusgs7hS/kVgenAS0SwR5vZ+Ti3An8HbnP32anW\nKHVFIS81x8xWBvYAPo+xB04fVmwR+CNRO10pnTwwlQj2l8gzb/FU/I3AP4C7tYBO0qKQl5pmZj2A\n7Yld8fahyOrkKLIWxiiMddB5fFk2DswAXgem4EymSCNZMrxJkeuJYH/E3Qup1imCQl7qSLIr3vpE\n4O9NkS0AYwh51iTHCGAE0CfVMqUSzQamJLfJ5JlLDsiTYSJF7gBuAp5zfaBKhVHIS91KWup+DtiZ\nDLtQZDgAK5NnDXKMJEJfm+fUn/nEFPwUYBJ5PkraMGV4Lgn1u4EH3H1uajWKdIJCXiRhZiOIqf3t\nybArRVYHYHAy0h9JhL5W7deWIjH9Pj25vUme98niGBmmUeR2ItTvcfcZaZYqsqwU8iIdMLPViNDf\nIRnpjwRgRRoZTo6hGMOAoSj4q8lsmgP9LYpMx2kkCzhZJlHgIeBBYsHc1BQrFVluCnmRTjKzYcB2\nwGYY44DN8GTZXl8aWa1N8GtBX7qcCPQPgHdpGqU3Mie5tiLDBxR5CHiM2KH9CXeflVK1ImWhkBfp\nomQh3xrAZjQFv7E5RfoB0Ic8w8iyMsZgYDCwEjHq1zX7pePALCLMm27vkecDLBmhg7EAeBznYZpD\nfboWykmtU8iLlFAS/CNoDv6xZNiAAqsBGQAaKDAYZ2VyrcJ/MNAjpcIrnRO7tM1KbjNpHeb5FmFu\nvEKRZ4kmsi8k91N1SZvUI4W8SDdIdtRbExiV3NYlw/rAehQZsPiFfWmkH0Z/cvQjpvzb3vfs7uq7\nQYGYWp9F9HiftfjrIh9RYDZZCslBEoAxH+OlFmHeFOhvaL91kWYKeZGUmdkgYN3ktg4wHGM1MnwG\nZxjFNmf3GyiwIkX6k6UfGXoTwd8ruXX0dbYb/jBOdICbn9zmtfN10/1c8szDmY8xP7lErUmGj4A3\nKDIZeAOY1uZ+hqbaRZZOIS9S4cysDzAMGP6pW4bVMQbh9Mfph9OrwzfKUaQHBXLEiYMMEfzxtSXf\nW/K1JV+HIk4BJ5/cxw0ak1seI0+m1Wi7NSfDJxgzcT6kyHvAh8ltJrE0blpye9Pd5y3HfzIRSSjk\nRWqImeWA/i1uA9q57wnkluGWoTnOF7W5bzlWbzlO/4jmAP8Q+FjnxEW6n0JeRESkRnU0tSYiIiJV\nTiEvIiJSoxTyIiIiNUohLyIiUqMU8iJScmY2xMyuMLPpZjbXzG4zs7XbvGZNM/u7mb1vZrPM7K9m\nNiStmkVqkUJeRMrhJmAk8AVgLNHA5i4z6w2Lr/3/D7HR6w7A1sSlfbekUKtIzdIldCJSUma2DvAK\nsL67v5w8ZkTDm9Pd/XIz2w34FzDA3ecmr+lHXF+/q7v/N53qRWqLRvIiUmo9iQa3C5seSFrQLgS2\nTR7qkbxmUYufW0iM7LdFREpCIS8ipfYy8CbwEzMbYGY9zOxUYDVgaPKaR4G5wM/MrLeZ9QV+QXwm\nDW3vTUVk2SnkRWS5mNlBZvZJcpsNbAHsQ+y2NxOYA2wP3EaM1HH3GcD+wOeT5z8i9tl7quk1IrL8\nckt/iYjIEt1EjMybTHf3hcAmZrYi0MPdPzSzR4H/Nb3I3e8C/n97d2wSURBFYfhMCSaCBYiYWISJ\nDViAvSiYLWsJNmAH1mEgiNkGmxkLY/A2EMFsd4PD98UT3OznPe4w57tX+L7nnF9jjE2Sj2MOD80s\n3gEHt1vGe0tyM+d8/efMdZaN+8s55/sx54NWvuSBvRtj3CbZZrk6d5VkneTld+DHGHdZwr/NcoVu\nnWQl8LA/Ig8cwlmSVZLTJJskz0ke/py5SPKY5CTJZ5L7OefTEWeEen7XA0Ap2/UAUErkAaCUyANA\nKZEHgFIiDwClRB4ASok8AJQSeQAoJfIAUErkAaCUyANAKZEHgFIiDwClRB4ASok8AJQSeQAoJfIA\nUErkAaCUyANAKZEHgFIiDwClRB4ASok8AJQSeQAo9QMAJCZOBu8P8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338fe1048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x1['x_5'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21339039978>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0QclrbNl9D+MY02LHEBGRNTIZCAdVH48bpPpU8t33HMbiYu+VJCJSQFOAhJfc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\nvIiISEH9f2p7USWCEPR5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21338ee90b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x0['x_6'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213390ab400>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1gJ8COxPeYJwNrA0cSxjJ304o/U2qkEWkURnwG8LfTS4OB2tJtWi6vsGZ2QnAD0MZaH3K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\nf/ZX1wPo6yjWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x213390b90f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x1['x_6'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2133909cf60>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zcB8AY3EfAm+nMA7AknxNBNm9MNwoTJrZjBtIOwdYgGv15OIS91pgA0Q2VV0m\nj+Ba2y1RWiG0RLZJ2onvm1H3W0u1cp9S+cFbXQNc4/vyCHDTqlWszcuD3XaDkSPxjjkGVqyAjz+G\n6dPZtHARhQV5+EHy6UQnBuuh9In3oQ99aEMbFrCAxSxmOctZuWklazatYTkbKIoWagklEq+Qt7Kz\n3YlATg7svjt06ADt25ddt2+PtGsH0aTZzsXFsGEDrFsH69e7S0EBbNzoLoWF7rJlizt5SJw4FBeX\nXtTzwPcrO3Gob8m/kJIQu0J9KjmBkArXFR4qvWiFQ5JuVNWiT5VEso9R/gRleeKAVCb5Dbj/rQdU\nEsSfgd+p6l0i0hx4HDd97zPgeFUtJsXqcuDdRmC/DBx41wlYAf8Gfh12OKZOrcIl7nm4xL0EVzpf\nhSuTF4BsBuJQWRJIlMlbUtba3l6ZPM1GsWT9Bf4E3FzJY+OA0SLkqbpMO3w4HHecy8YAvg/ffguf\nfQYzZxJZvlJ16xZRlChR9mIv+tOfPsFXZzojSb+ATWxiIQtZwAKWsISVrCSffNaxjs2RTZREtlLi\n+eWqtZEItGkDHTuiOTlIhZMAOnRwl+xaLDdSVOROFhInDhs2uBOHggLYtKn8iUNyxSFx4hBUHMT3\na1VpTrQUTepkqWp8x4ftmIjMwO31EsF9aHyGO4noo6pHpOI9qhVPdZO8iBylqp9U8dglqvp48P1M\n3JnJ0sqObahEpBVQ4Jbu/23Y4Zhq8XGdzT9Svky+ktL+7chGhaLKy+RQvkzekqqTdgsa/NjMrL+i\nl/rI9paxfA24SoRlqtCuHQwbBiecAE0rmWK6aRN8+il8+SXMmUN01Vo83zVcWtCCvvSlH/3oHXy1\n2cEiLHHiLGUpC1jAIhaRSy6rWMU61rFRCtga3Uzc9/AqnIA1bw4d2kNOp7LEX/GEoFUrN9agPhQW\nwtq1cNNN7j0XLXInLO3auYpD164upjlz3ElD+/buxGHdOtdjkgIVyv+NTpGqpmzhExG5Arfk+8O4\nkv2NuAF456vquFS9z07HU4MkvxW3usINqloS3NceeBY4QlXbpTzKNCIiEcCDJ3EzIUy4inB923PY\ndhrYGsrK5MVVl8mbkVwmr7rF3Zy6L5Onkejt6JlbkZd24th3cWt0LhRxnexnnw0nn+wy6vYsXQoT\nJsC33yILFhEp2IgXlKZzyGEAA+hNb/rQh73Zm2yq3wzPJ595zGMRi1jGMlaykjWsoYANbCntHij/\nt5GV5bqutUmGAAAgAElEQVQHOnZ0XQTJ1YDE9S67lO8eqK1PPoE773QJ/MorYf58d040bhy0bQtP\nPgn5+TBmjDt+yxY4/3w3POKoo+Dvf3fxHH44XHQRvPsuPPMMHH00dO4MS5a4X7XnwYEHunEO333n\nHvN91+MCrtKxdWtKfqSGUnHIU9WUzk8XkV8Bd+BWG1oMdAf+T1WfTeX77FQsNUjyhwH/xPVNnwP0\nAJ7Gzdk5T1UXpzrIdCOStRZubgc3hR1KhlqLK5PPpaxMnpgGlhhNnpgGVsnTsyjfv729MnlTGsbH\nUBjuhaMLykYK7YxPcOt2zhZxCf7MM+G001zi3xm+D9Onu+w2cyaRZSvKlfn3ZM9yZf7d2b1cmb+m\niihiPvPLdQ+sZnXQPbCR4kq6B0Rc90CHDq57ILkqkHxCUFlRoypvvQUPPOAGHO6zD4waBb2C5Tju\nvBPy8uDee8uOP+00122gCi1awK9/Db/7XVmXxFVXuV9nQk4O3Hor7LWXuz1pEjz7bGlXAldd5X71\ne+0Fe+/t3nPVKvczjBgB+++/bTfFxo1lXRXJAyOLitzrbt3quik8r6qBkaGbqaoD6vINRGQq8KGq\n3liX71Ppe9ekT15EWgKP4QbfRnDZ7i6tqw7+NCOSPQ3OGwhPhR1KA+HjRuj+iOvfXohrba/Arcy1\nHqRAkSJBvcrL5E2AFkmt7e2VyVO+xUMj9TD0Xw3f1+CpU4BLgBki0KQJnH66u7SpwTrohYWub3/K\nlKDMv6Z0NH9zmtOHPvSjX2ni31GZv6Z8fJazvLQqkOgeWMtaNsoGtka3aIkfl4rdA82auWSfk+Mq\nA5VVBVq3LuseGDIExo51LfIdWbnSJdVZs+CJJ1wFYMgQ99j06e51LrwQeveG5cvhoYdcb8q5VSyH\nP306PP443H+/G2Zx882uinDZZfDiizX756tKYmDk2rXuuqYDIz0PqWXmmaKqKV9ONiHIl0uAm1X1\noR0dn2o1HV2/D3AgrkOzM27pt+Y0miHnJfNh4b40quJtRcW4Evlsyvq3c3H922tw08ASZfJKTt2F\nRJk8MTBNKk3aLXF/WdHSZ5n60qxscdTqOgS3KOt3qlxYVMTXL70Er7wCp5ziWve7VmN3zxYt3KC+\n444DgnHmy5fD+PFs/vZbvl04n+kbppeW+TvSsbS135e+NS7zVxQhQtfgaxsKxN3f5zrWMZe5LGIR\ny1nOii0rWLN0DQuXrmdWdJOWUCIlFboHYjHXB9+xo2uVv/++K59X7B6IVfjE7hQUmXv0cH3048aV\nJflnn4Vjj4Xjjy87ZssWVwmoLMmXlLgqwo03ul+v58GAoH3btas7kTg0hakwO7tsTEQNbfN5sLMD\nIxMnDfPnQzxOSke0i8jduJHZi4HdgVtwcxL/XyrfZ2dVO8mLyPW4oJ8ArsP1OTwPzBCR4ar6RWpD\nTEuLYYFHxiX5AspPA1uCS9yrgLU7v1paWZm86kVXEhtnmfTVwk2Wqk3H6n64rVFm+z4XFBfz+Wuv\nwRtvwIknwllnuaxWE7vv7jLVueeigOf7MGMGTJzIqpkzmbDsS/2k6JPSMn8Pemh/+ksf+tCb3nSh\nC5E6+u/bjnYcHHxtw3O/ymKKWcAC5jOfJSxhRXwFq1evJn/1OiCPKZOFzz/XbboHWrWquntg7VrX\nwk0oKtp2zEAk+JFVtx1Y+PzzMHiwK9PPm1d+caN4nErP1dNN06buxKfTTvawn3AC8Xic91McRhfg\nJdwKdjHcoKFDVHVNit9np9SkT34Fbp7fe0n3ZQG3AaNUtUmVT84QIjIKsu6Dokh653kfl6Rn4xL3\nQlzxJblMvgFkC1WWybPZudHkLbEyeaZ5F5jqBt+kas3PxcDvgPGRiMsyQ4e6Efmd62CX6s2bXafz\nF1/A7NluNL/nRpQ1oxl9g69Emb9tSncb3bEXeZFJTGIJS8gmmz3Yg9/wG27mZi7ncvZjP7awhXWs\n43VeJ598OtKRtawln9WUSLHG1d/m/KtJE/erLSlxiXrPPeEXv3CJ/b//dX38RxwBTz/tTgSGDoVf\n/cqV5p980rWCr7vOlcwvvthVGG65xZXrq1OASXeFhW4MAzBMVXdmfOlOE5GDgH/h5sx/oqpXp/L1\nqxVLDZJ8e1XNr+KxI1V1YkoiS2MicjLwlkuWKd80aAfiuH7t2cH1Ilz/dnKZfCdWS2uJ0hKpcrW0\nRJnclktqvD4DPnZ/Yd1T/NK5wEXAe5EIqgrHHOM6gbt1S/E7VbBiBYwf7+bwL1hIdP1GvGB52A50\nKDeoryc9aULdtVmu53qGMIRe9GIWs7iLuwDKVRh+yS8ZzWju5E7yyONe3Ki7l3mZp3m6dInhznRm\nb/amDW2Yy1xyyaWEEgq304PaqpVL2suXu3EDP/sZDBoEr73mSvzt2sE//uFa8RdcUFb2zxQ//giX\nXw7Agar6TapeN+iD/wY3BvUmYFqDSvIGRKQfMBMmAEem4BU34QalJaaBLcUl7qBMznqIFFZdJo/i\nEnKLnZgGVh+rpZnM8D3wOnwNDKqjt8jHDdB7KxrF9zw48khXhk8M/65rvg8zZ8LEifD990SW5qJF\nm1GUCBF60EP70U8S/ft1WebfwAZO5VQe4AEGsP3B3mMZWxrL53zOEzxR6XFnczanczonciKLWMR8\n5jONaUxkInuxF+tZz2pWoREfRbcpyQfdA+TklF9TIHngYIsGuqH4+++72QNAS1VN2XgyERkHrFbV\na0XkE0JO8tZOq5k5ECmB77KqTvIrKZu/nbypyGrKpoFtYadXS2u5nYFp2SQ6Ta2X26ROsOJFpWW7\nFGkPvA4UeB6XAf+aNAlv4kQ3wuu889yw8LoUicC++7oLwX/FoiKYNAl/8mTmz54ti/I+4B3vHcCV\n+XvTu9xo/nakZmmQTWxCEFrRarvHvcd7rGAFN3Ijz/P8Tr12NtnsE3z9jJ8xmclczdV0pCOXcRlX\n+1fTk55cwiWcy7msYQ255JK3MY81G9fw44INTI9urnTJ4SZNXEVge2sKtG1bNh4gXSxcCLEYy0tK\nUprgzwIG4gampwVL8jWgqiUiWbPh/v7wCq5MvoPV0pI3FUle5rSqMnkDXy3NZIB6SPIJrYEXgSc9\njyuB56ZOJf7FF65+PGJE2TDv+tC0qes+OOYYIBjNn5cH48ez5ZtvmLZgITPWfV9a5t+V9gyoUOZv\nSjUmxwOK8hAP0Z/+7MEeVR63jGU8xVM8yIM1rii0pCXXcz23czvFFDOUoQxiEHdzN2dwBl3owlu8\nhYfHeZzHkYmGTDBocBObmMc8FrKQpSxlxdYV5Ofmszx3A3OjG7VYtkrcKz9oMBqFtm2gQ8eqqwK7\n7lq7JYera948fM/j61S9noh0wW08c0xiobh0YEm+xuJxWAgtFyaXyateeKWRrZZmMkAL9ydbH0k+\noTluLcmHPY9rgcenTaP4m29gwABlxAjhgAPqb73ZZDk5bhW/s88GgtH8P/wAEyey5vvv+XTp10zc\n8imKT4QI3enOAAaQGM3fjW7bTcr3cz+LWcw/+EeVx/j43MqtjGQku7M74E4OauKI4CthOtNZwAJG\nMYrhDOdmbqYtbbmMyxjIwHJrD7SkJQODr20EJwJx4ixmMfOZz2IWk+vlsnrtatauXUvu7O3vSJhY\nU6CyE4EOHVz3QG3/BFRh7lx8Vb6r3SuVMwjoAHwrUhphFPh5sNRtkzDWkrEkX3NPIDzMKIRsK5Ob\nzBQVWB3CsJ1s3NrZ9/o+NwIP/vCDFF17rSvfjxjh5nqFkewTIhFXXQgqDKVl/s8/x588mYWzZ7Nk\n5Qf6jveOADSlaWmZvze96Utfdgn2832AB/iSL3mAB9iVqoevb2Yzs5nNPObxAA8ALskryrEcy93c\nXXni3YESSniAB7iRG1nOcjy80jEBXenKLGZxKNWbIB8jxl7B1zbcmgIoympWl1tyOG9THms2rWHO\nog3M2IkdCTt2rHzvgYo7Ela0ciVs3EgMUteSxy0OWbHk9BxuwNUdYS0WZ0m+5r5AEVYCdTwg2JjQ\nRNB8L7yT2BhwJ3C77/M34M45c9g8ZowbmDdihFsWLl06e5s2dQvFH300AB4IeXnwyScUffMN0+cv\n4Pv1M/E0UebflSY0YQMbuJqrd7hSXwta8AzPlLvvLd5iOtO5hVvoVMOZPs/zPIMZzN7szTzmlS4q\nBK5F7lc6aKj2BKFj8HUYh217QPB3V0ghC4KvJSxhZfFKVq9cTe7K9cyPbKp0yeHEjoQdOqCdOm27\nI+GcOaWHTk7VzxMM3ptV7mcUKQTWqOqPqXqf6rIkX3M/IGxhMc0syZtMFc9G8lO503YNRXBb3v7J\n97kPGLtwoW64+Waha1eX7H/xi9TuFpMqOTlu0Z+zzgKCMv+PP7oy/4cfwvpcBGEsYxGELnShf/DV\nhz58yIesYQ1jGIMg2/TXt6Md2WTTPWmSY5w4i1hU+n1ig55mNCst8ycsYhETmMCTPAlQ2q3wLu/S\njnYsZSm9qePBjzvQghYMCL624btLYkfC0u4BP5dV61axbt06mT63gOLoZi3xvXJLDkejrI/H63yB\nmtCnr9kUulqQiPyHbhzH+aThp4sxKXA/HLHeTZlPN48AN4mwVhV2282Nxj/mmG3Xfk1XQ4aUdTmo\nlm4wL5GYqh8XcHPmm9OckzipdGBfckl/HOO2mUK3kpWcwznbbNyzH/uVzrNPGMUohjGMwQwuvW8K\nU7if+4kT5wIu4HgyZ4L8GtYwj3mMZaxfSOG/VfWUsGOqa5bka0FERhHhPq4nYqu9mYz0GPRcic5J\n4+mZ44DRIuSpuprs8OFunfv6HKqdavn5btGer7+G+fOJrisoLfO3ox396V+6Wt8+7EMzUrYdesZb\nzWrO5EyAM1X11bDjqWuW5GtBRHoDPzIM6Bl2NMbUgXGwy0I3STTdvQZcJcIyVTfyatgwt+VadfZ6\nTWc//eQ2nZ8xg8iSZbB5Mz4+gtCNbuVW6+tOd6JWYKzUB3zAHdwB0KGq1VsziSX5WhARIUIuB9OJ\n48KOxpg68BpEZrrFlNO2KV/Bu8AVwEIRNyfr7LPh5JPd/vaZpLgYJk92l59+IrpitXrxIgFoQhP2\nYR/tT3/pTW/60IcO1Hy7t0xyO7cznvEzS7SkHhdfCI8l+VoSkafZlfP4vQ1iNBnoA+ALtxtd/W7f\nUnuf4BYPny3iEvyZZ8Jpp7nEn6nWrHGt/a++cmX+tQV4wbosbWlHf/qVlvl70avRlfk9PE7jtHgB\nBfep6h/Djqc+WJKvJRE5BXiT/wM7UTYZ5wvgA7c4c0PtkZqCWx9/hohbg/X0092lzfanrGWM2bNh\nwgT47jsii5cpmwslUebvSlcGMIBEa38P9sjoMv83fMO1XAtwsKp+FXY89cGSfC2JSFOEfH5OC44K\nOxpjUuwn4GU3mbh6y6Gkn++AC4GvIxE3Av+UU+C3v3UrqzQmxcXw5Zfw+ecwaxbRFavx4kVAYo37\nXvQLWvt96ZtRZf57uIf3eX+ph9e9tovTiMiluGLRHsFdPwB/VdVU709fK5bkU0BEnqMdwxhFrMF0\nXBqzM/KAR+Ed4MSwY0mR2cAFwOeRiFs15cQT3Tz2jh3DDi08a9e61v7UqTBvPtG1G5LK/G3pV6HM\n35yGN74hTpxTOTW+iU1/V9Xra/t6InICbmuDubghKyOB64CBYS5+U5El+RQQkeOA97gE2C3saIxJ\noWLgNngGOD/sWFJsMfA7YHwk4uarH3ccnHMOdO5c+xcfN85dknXrBs89V/Vzpk+HRx6BRYvcIjrD\nhrmYkr32GrzzDqxa5bobfv5zuOiisumCH34ITz3lltgdOrR0w3TAreX6xz/C44+7DeR3ZM6c0jK/\nLF6KFBaWrn7Xha7lNuXpQY+0L/NPZSqjGQ1wgKpOq4v3EJE1wLWq+mxdvH5N2GCx1PiYCOuZSVtL\n8iajZLsdNjJxnlF34GMg1/e5CHjv/ffRd991C+oMH+6Scm306AF//3vpIjc7XEx9zBg3C+BPf4Jv\nvoF77nFrsB4Y7Fr60Ufw5JMwejT06wfLlsEdd7hqxGWXwYYN7v3GjIFOndz1AQfAIYe4599/P1x8\n8c4leIB99nEX3LJtWlzsWvqff86yWbNYsWIC75W8B7gyf096lk7j601vOtJxmwV5wvQ+72uU6DwP\nb3qqX1tEIsCZuD2Wvkj169eGJfkUcFvPyst8x4UcTcx2mzOZJCKQn8EFv87Af4F8z+MS4K3x4/E/\n/BCOPBLOPdetk18T0ajbSH1nvP22qyBceqm73a0bzJzpWu6JJD9rltsQZ8gQdzsnx33/00/u9ooV\nbubAkcHWsAMHwpIlLsl//DFkZcERR1Bj2dnu+cFreADr1sGECRRPncoP8+bz05o3Ssv8rWldumhP\n7+CrBS1q/v61sJGNfMZn6uE9mcqNYkSkPy6pNwU2Aqeq6k+pev1UsCSfOk+yiUuZAyEv9WxMSmkM\nzS9JoyZZHWkPvA4UeB6XAf+aNAlv4kQ49FC3Pn6vXtV7wWXL4IwzXHLs29eV1avq9581y7W6kx10\nEDz8cNntfv1ca/6nn9xufLm5bgDd0KHu8S5dXJl+3jz3PrNnu8WANm2CZ591LflUa9cOTj3VXQgS\n//z5MH48Bd99xxeLZzJl05SkMn+Xcov27Mme9VLmH8/4xMY7z6f4pX8C9gPaAKcD/xSRn6dTorc+\n+RSSqEylO4MYYW15k0Huhl8Xwr/DjqOebQZGAeOiUeKe51rU551Xur3sdk2dClu2uBb5mjWuL37N\nGnjmmcrL5eedB8cfX7pfPeAS+A03wHvvlfW5v/EGPPaY6wLwfTdo8A9/KHvOpEkuoRcXw7HHute9\n+25Xjdh7b3joIfA8d3+ixV/X4nH3s0yeDD/8QDR3FV6J2/Uoiyx60pN+9CtN/DnkpLzMfzEXx+cz\n/3+eeiek9IUrEJEPgXmqelldvk91WEs+lXweZCHPk49rFhiTCZpAXiFKw1n0LiWaA08Bj3ge1wBP\nfPstxV9/DQMGKCNHCvvvX/We9gcfXPZ9jx6u5X3WWW4g2/E13PBl+nR48UW46ir3esuXu6T9/POu\nWwHKldNLn7NgAYwa5cYZ3Hyz60K47DJXzq+PtQJiMbcl8OGHA0Frf/16mDiRkqlTmTV3LrPz38IL\nlpFvTWv60rc08feiFy2p+QJGs5nNXObGgKdr/8PsUARoUg/vs9MsyafWq0R4kK9ol0EbN5nGrims\namQJPlk28A/gPt/nRuDBH36QomuucYl2xAgYPLjqZJ/QsiV07eoSc2XatXPT2JKtW+dW6ku04p99\n1rXOEycJPXq4asG995Yl+WQlJfDAA3Djje59Pa+sCtG1q+siODSk1Q/atnWDDE8+GQgS/4IF8Mkn\nFEyfzpeLZjF101f4wd72nenMAAaUK/PHdjJ9vczLGiW61MN7O5U/gojcBrwHLAFaAcOAI4FfpvJ9\nasuSfAqp6lYReYxv+SNDiKbX+ZwxNdQc1u74qIwXA+4Ebvd9xgJ3zZnD5jFjXCl8xAjXUo1U0VO3\nZYtLtL+s4vO/Xz9X0k721Vfu/oSiom1H6CfeT3XbE43nn3cnIHvv7frpPa/ssXjclfvTyZ57ugvB\naP543O3C99ln5M6aRV7up/q/4v+JosSIsTc96Z9U5u9Ep23K/LnkMpGJKHqnqnqVvGttdMRtgrgb\nsAGYAfxSVcen+H1qxZJ86j1KCdfxFVCLgazGpI2WbthwHPvAAFeP/TNwk+9zLzB24UItuPlmoVs3\nl+yPPNJNdTv0UDcCPj/f9cnHYmUj45980t0/Zoy7fdJJ8NZbbg778cfDt9/Cp5+6KXIJhx0Gr77q\nTir69nUD+5591t1fMcEvWuS6Bp580t3u1s2dELz7rqsaLF3qKhHpLBZzMwOCKYAeCAUFMGEC8alT\n+WnuXObmv8Nr/msAtKJVaZk/sUzvq7xKhMh6D++5VIenqhem+jXrgg28qwMi8ihNuIiriJIhu1ya\nRuxj4DO3+F0jXhNuux4BbhJhrSrstpsrR69eDQUF7vv+/eHCC91jAHfeCXl5rtSe8N13bjT94sXQ\noYMbHJfc8vd9eOEFt+BNfr7rTz/sMLjgAmhRYWraqFFuMZ3Bg8vumzLFjbCPx91zajo2IN0sXOhO\naKZNQxYtQTZuKi3zC4Kif1HVW8INMjyW5OuAiHRBWMhRxPh52NEYU0tfAf91C3P3DTuWNDcOGC1C\nnqpL1MOHu1XrEv3qpu7F424xoUcfhcWL40BPVV0UdlhhsaledUBVl6E8yiQ8isKOxphaCtZzycRV\n71JtBLBSlVeALvn5cN99blT966+7PnVT92Ixt1LfihU+cEdjTvBgSb4u3UEJXnotcGhMDQSbtK0O\nN4oG5QxgqSr/AXqsW+fK8L/9Lbz8MmzeHHZ4me/llyEe3wLcF3YoYbMkX0dUNRflYSbjYf+nTUNm\nLfkaOwFYAHysSq+CAnjiCTjzTPjnP91KdCb11q6FN9/08f17VbXRTwyxJF+37iROibXmTYMWdaPq\nLcnX3BDc+qeTVRlQWOhG259xBjz9tNtYxqTOuHHgedaKD1iSr0OqmofyIF/gURh2NMbUnERQS/K1\ndyhuMvU0VQYVFcFLL7mW/WOPbbsYjqm+H390W/H6/g2qui7scNKBJfm6dzceRXyETWMwDZYfQyzJ\np85A4GvgJ9/n8OJiN//9t7+FBx90e8Wb6vM8uOcej2h0Bm5Wo8GSfJ1T1XyUq5mGMD/saIypGa8J\nWOpJvV7AJGCR7zMkHndbzp5zjttLPjc37PAaljffhAULInjeRaoaDzucdGFJvn48iTCBt4mzNexQ\njKmBJpCHVaPqSnfcmkPLfZ/jPQ95/303x/6229ye8Gb7Vq+Gp57ygEdVdWrY4aQTS/L1QFUV5QI2\nEufjsKMxpgaaQX4j3qSmvnQG3gVWeR6nqRIZP94tlXvLLW4DF1O5hx5S4vH1wI1hh5JuLMnXE1Vd\ngHI9U4HFYUdjTDU1hzVhx9CItAdeB9Z5HucA0c8+c0vR3nADzJ4dcnRpZsoU+PRTwfN+r6rrww4n\n3diytvVIRKJE+JzWDOJyYthKl6ah+DfwDWwGmoUdSyO0GRgFjItGiXseHHiga+H37x92aOEqKoIR\nI+Lk53+K7x+jltC2YS35eqSqHj4j2YAyIexojKmGVu7KWvPhaA48BRR6HlcA2dOmwe9/D3/4g9ux\nrrHmthdegNWrFd+/1BJ85awlHwIRGQ3czoUIXcKOJo19BvyIW4UlC+gKHIOrZSZMAGbidnOO4jo1\nh8B2f6/PAYsquX8f4Jzg+69wc5wSxb8OwJFAz6TjPwcmB98fDhyW9NgyXOfqhWTGqfR04C2Yhpv+\nZcIVx3U+PxiJUOT70KePa9kffPC2285mqnnz4NJLFc+7pTHvMrcjluRDICIxInxFO/pzGTHbpLsK\nLwADcInbBz7CzeO6Apf0Ab4HWgDtcJ98X+C2S7sS1/ypzBYIdqJ0NgOPAScD+wX3zcENM9sVN6Z8\nOi6hX4pL+Hm4ptWw4PEXgYtxe7H6wBPASUHsmWAx8Cx8iDvPMunBB8YCd0UibPZ9t9f8yJFuC9pI\nJpxdVmHzZrjwwjirVv2I5x2kqjZvqQoZ/FeQvlQ1js95rAE+DTuaNDYcl3Q7ADnAKbgWe/L04QHA\nnrgk3wEYCmzFJeGqNANaJl3m404akvdR3QfXat8Fl+iPBrJxLXRw1YUcYA+gR/B9YrWYz4P7MyXB\nQ+kmNbYgTnqJAH8GNvo+dwGtFy5UbroJzj8fxo93C8RkovvvV/LySvC80y3Bb58l+ZCo6vfArXyG\nliYOs31FuNZ1VSO/PFyJvSku6e6sabiThawqHvdxFYMSyroBOuI6qDfgSvprg/vW4lr9Q6rx/g1B\nC/dhYUk+PUWA64ANvi8PA7ssXQpjx8K558L777s91jPFBx/Ahx8Kvn+xqs4JO5x0Z+X6EIlINhE+\npxkDuYQYrcOOKI0p8BJQDJxf4bE5wGu4JNwKOIudb0UvA54GLqrkOXnBY3FcK/43lO+T/xrXPSC4\nRckHAf8EDsadcEzEjRM4DrfaSQOX9RcYA1jnZ8MwDhgtQp4qdOzoFtcZOhSyG/C0niVL4KKLPIqL\nX1DVkWGH0xBYkg+ZiHQiwjRy6MD5RG1aXRX+A8wDLqB0pHepEmAjrm/9W9zenhfh+up35N+4RH9Z\nJY95uJb6VmAW8A3uBKNDFa81HZiN21/0IVwf/QbgDeAPuITfgGWNRS/ykIfDDsRUyyvANSIsU4Vd\ndoFhw+CEE6BJk7BDq57Nm+HSS+Pk5i7C8/ZX1Vrt1SsiY4BTgd64kTqTgdGZVh2wcn3IVHUlPiew\nkv/f3p2HSVWdeRz/nupuRMENNQYREzfEEVBxA5dxiRPjgsYxJiaTKImZbGZ5lNGsY2bUGHXco6AY\nEVwSUOIWFxCXGBAFAUVERUBUFkERaKBZuu+97/zxXqBtWRrp6lt16/d5nvuUXV3cOmB3/e4595z3\nRDyCqXDoejwOTAf68umABx9m74APpZ+O/1S/0ozz1uMz83tu4PtV6Xk74vfkPw+M28Br6/Ce+8nA\nXPw+fgf8fn1MLtaexTXapKYcfR2YbcbfgT0XLYJbbvGd74YOhZUrs25e85jB1Vcbc+fWE8d9tjTg\nU8cAfwKOwOeT1gBPhRByVQpCIV8CzGwSxreZStBEvCYex3vH5wE7NPPPGD7EvilT8QDu0QLnHYkP\n2W+H38NPGn2v6ddlKmmz8fmMUtpOwwe5njGjy9KlMHCg72l/zz2wvCUys4iGDfOqdknyHTN7qyVO\naWanmNk9ZvZmOkeqL7AHfuMtNxTyJcLMhgO/5zl8aFh8iH4Kfi+8DbA8PRrS79fju3rMwSe/zQMe\nxofuD2h0nofw5XdNvYIP1K3vuv1pfNnYEjzZnsbX1q/vgmAmPuHu8PTrTvgMten4ffsCn1zbX67a\nwoKm+oYAABP4SURBVIdF3qRmND4Y0wn/Z3t0I6/9UfqamzdxzjeAr+GDKs15/VXp6y5q8vy1+HzO\nzwPXN/neOOAwyuNa7gT8unmsGd3r6uCuuzzsBw2C2tqsm/dpkybBwIEGXGVmDxbxnXbAf74XFfE9\nWp1WaJeWywl040HOYkcKdMy6ORmbgE9qG9zk+TPwiixrpntPZl291U7A9/jkffNaPr21ykJgNvCd\nDbx3HX5xsBzYCv90/w6+XK+xBuBJ4OxGz22HD9s/gv+GnUk+ftO2Kf4mNXX4/9rzgX/fyOsewoO1\nUzPOuQLYGx+2vnATr30ZL3FwYJPnp+BL1Z7Ag/xUfLXmAfhg0I/xsgnl1GvqDbwGvGrG91etYuJ9\n93mP+cwzfTi/Q4esmwjvvQeXXhoTwvOY/a5YbxNCCMCNwBgzy1U3SxPvSkwIYRsKjKUdB/BDqmmf\ndYtEUvdDzRs+D7E1aqoV8IGZ05s8PxcPqJHAKXhw/7yZ59xzI69fjo/TDsALzBzMuh77A8ANrCtw\n2AtfsnYW8Ee8RtMNzWxDqZqGXx+PLRS8kE6fPnDOOT4zPwsLFsBPfhJRWzudOD7KzBYX661CCAPw\n67ajzOyDYr1PFsrpwrMimNkKEk6jjsX8lbhZ95ZFWsO2PnCR5d1bA84FLgH2b+FzXwD0Yf0lDrrj\nKzXn4HdxpqfPzcSXql3Rwm3Jwn54Had3koQToggeeQS+9S247jqYN29Tf7xlLVkC/fpF1NbOJ45P\nLHLA34JfLx6Xt4AHhXxJMrM5JJzGPBL+rhn3UiLSOg4fZdiEq/DpGT9t4fMOxVdA/nED3+8KXIlP\nwf5K2o4u+LyAa/A7Nt3xkYDRLdy21rYnPtVlbpJwchwTnnzS19hfeaWvUy+2FSvgkkti5s+vJY6P\nN7OiXWGkAX8GcLyZtcJfrvXl4U5hLpnZ+BBCXyZzHx3wzVFEsrS9Pyzk01MTWsNEfNJcc1ZHbo45\neBmDp9lw0UPwsgc/aPT1EPy6pxfeC54IvI/XYnp3E+cqB7vhcxAWxjE/AB5+9lls1Cg47jivpLdX\nEX4K6uvht79NmDlzVbp17IyWfxMXQugPfBO/I1QXQlhTJ7PWzFYV631bm3ryJczM/gJcynP4GmyR\nLO3oD1mtlR+DjyJ0xgO0Bh86v4gtu+iYmJ63Z6PzPg/chI8arG8gbSFwGb7Iehwe8nsBx+G3NPJU\nTWVnvJ7TkjjmW0Bh9Gg4/3z4zW9g2rSWe6M4hiuuMCZPjkmSU83s1ZY7+Xr9CL9O+we+NmfN8fUi\nv2+rUk++xJnZ5SEEeI7LSPBPkQrZSVJKzE7+kFXInwv8W5Pnvpw+37TS8eY4EZ8931hf/J7/r1j/\nr9tFQD+8tzuedas6wUsp5HFbmO3wzRbviGN+DgwZP57oxRfh0EN9m9tu3T77yc3gxhthzBjD7Gtm\nVvRujZlVRCdXIV8G0qCv53muIsarrynopbW19SKAxQz5Orx68Zre8zv4CskOeA9+xyavr8HXrTfe\nUuA8fGndlenXDfhaecNLK8xNz9keX1rXjk9uQEj63E6sf3LfKHzi3d3p14cBbwEj8OH6arxnn1fb\n4MsF+8cx/YCBr7xC/YQJ0KOHcd55gYMP3vw97QcNgsceAzjfzDZWHkE2U0VcyeSBmV0N9GMM/imj\nyXiSgUIobshPwJeuHYJfx/bDh9F/v4HXry9KZgPzG309r9E55+NFbXri2xtsyIYiahW+/G5go+c6\n4cP238Un7t2Nl1bIuzb437sujrkEaPv664F+/eCCC2DcOO+dN8fQoXDvvQAXm9ngYrW3UmmdfJkJ\nIfwcuIkj8Gm+6tFLK6q+AvtuRBi46ZdKhUnw+gLXFAqsSBLYe2/o2xeOPNLX3TdlBoMHw913A/zB\niljsppIp5MtQCOHHQH8OwyuraTxGWkm4Bs5Y4RXnRNYnAa4DrigUbGmSBPbYw+/ZH3ssVKVbMSYJ\n9O8Pf/sbwK/N7KrsWpxvCvkyFUL4T+B2DiFwKgp6aR03Qq8l8GLW7ZCycCtwaQgsMoOOHeHcc+GE\nE+CGG4wRIwAuMLMBGTcz1xTyZSyE0BcYxEHA6QQFvRTdbbDXfK/0JtJcQ4BfhsACM2jbFlavTjA7\nz8zuzbpteadYKGPpJJVzeRV4GMvluh0pLdvkbIsuaRXnAdPM6AIJq1YlmP1YAd86tISuzJnZvSGE\niNe4j+UYZ1NY79apIi2hHSzF14FXZd0WKRvzgC9DNBNWAqeY2Zis21Qp1JPPATMbCpzELJZxOxEf\nZt0iya3tfGLVkqzbIWVjMnAYRNNgYQy9FfCtSyGfE2b2NEZPlvI2A4l5M+sWSS6lm9RkVfVOystw\noBckH8LUCA4zs6lZt6nSKORzxMzeIeEIYh5mGPAc3u0SaSk7+INCXjYmAS4Fzgbq4YEIjjSzORk3\nqyIp5HPGzPzOPPyO5zGGkpCb/ZQkcx38IcvtZqW0LQPOhORyr8v56wS+aWYrsm5XpVLI55C5PwCn\nM52V3EGkrpe0CPXkZSNmAodD9LhPsOtjZleZ1mlnSiGfY2b2GMahLOY9BhIzPesWSdmr8SU5Cnlp\naiTQE+IZMDv2+++PZ90mUcjnnpm9RcIhNDCS+4DRaHMb2SKhyJvUSHmpBy7Bt9Kog2ciOMTMNPW3\nRCjkK4CZ1WL0Af7AM8ADGKuzbpWUK6vGFPICPjzfG+JrvXTCxTGcbGaLs26XrKOQrxBmlqS7PJ3N\nm6yiPxGzsm6VlKOoDUET7+Q+oAfEr8Ec8/Xv15qZ1vOUGIV8hTGz4RjdWcZ4hgBP4ONtIs3VFhbo\npk/FWg6cB/ZtYCUMi6CHmb2cdbtk/RTyFcjMZpJwDPALXmY1txLxbtatkrLRFj6EkHUzpPVNAA6C\n6D5YBfQ1+LaZLc26XbJhCvkKlQ7f34zRjWW8zGDgSdSrl03TJjUVZyXwS+BwsPdgagwHmdkQLY8r\nfQr5CmdmM0g4GriQ8aymPxHvZd0qKWntoQ5dD1aK0UA3iK6FyOB3aXnat7NulzSPQl7W9OpvxOjO\nUiZwFzACfYrL+m3rDx9n2wopsmXAT4F/Bd6HiQl0N7Mrzawh46bJZlDIy1pmNj3t1fdjHPUMIOL9\nrFslJWd7f9Ayuvx6CtgfogF+7/0XERxlZm9l3S7ZfAp5+QQzi83seowe1DKJQXgpK/XqZQ2Vts2t\nBfjM+ZOA+fDPBP7FzG42szjrtslno5CX9TKzaSQcCVzMSzRwCxFT0cIpgZ38QSGfH/XAdcDeEP8F\nlgLfj+FEM1M1jTKnkJcNSnv112IcwDJG8gAwiJh5WbdMMrWdr59TyOfDk8ABEF0MVgcDItjLzO7U\nzPl8UMjLJpnZdEvsNOAk5jKDgcDDGMuybplkouCb1KjqXXmbAZwGySnALHjB4EAz+5mZaYVkjijk\npdnM7CkSugEX8Bq13ETMaEBzbStPQfXry9Uy4FfA/pCMhPnA2TEcb2ZTMm6aFIFCXjaLmUVm1p+E\nvYj4E88QcxMRk/AtKqQiJDUari83q4AbgS9C9H9QH8FlEexrZsM1NJ9fCnn5TMxssZldCHSljgd5\nFOhPxJtocl4FiNsQPsy6EdIsDcAdwJ4QXQTJIhiSeLj/r5mtyLp9UlwKedkiZjbDEvsGcCiL+CfD\ngD8TqxZ+zvkmNVLCEuAvQBeIfgAsgL8Z7G9m3zczVcCoEAp5aRFmNtES+xLwZT7gdQYD95KomE5O\nba3h+lJlwCN4Kdr/AN73+pUHJWbnqBxt5VHIV7AQwgUhhFkhhJUhhJdCCIdt6TnNbBQJPYFzeId3\nGATcQcwbeNdC8mEbWJx1G+QTYuABoCfEXwXehjFA79isj5lNzrZ1khWFfIUKIXwDr3/xe+BgYDIw\nMoSw85aeO62FP4yE/YA+fMBL3A/cTMTLaDZ+HmwLqwHd0M3eKuB2YB+Ivg5M8T1lTozMjjezl7Jt\nnWQtaFJlZQohvASMM7NfpF8HYDZws5ldU4T3O5zAxRhn0ZaYXlRzGNCupd9JWsU/gWfhXeALGTel\nUi0GBgDXQ/QxVAV40OBqM3s567ZJ6VBPvgKFEGqAQ4Bn1jyXLqF5GuhdjPc0s/GW2NnAvqziNp5n\nNdeT8BjazqwcqX59ZuYA/wV0gvi/oeFjuBPokph9TQEvTSnkK9POQBWfniC9APh8Md/YzGaa2c8w\nOhHzP0xiEX8ChmLMLuY7S4tSyLcqA54HvgG2J9iNsGwlXJ3AHmb2IzObkXUbpTQp5CUTZvaxmV1O\nwu7AD3mbWdyJL797E03SK3XapKZV1AK34Nu+Hgc8CLMiuCiGTmb2WzObn20LpdRVZ90AycRCfDLu\nrk2e3xUvc9lqzGwlMDCE8GegD/P4FcPoRXsiDqKaA4FdWrNF0iztvIegkC+OV/D77fdAvNr3A3oU\nuDWC51SdTjaHevIVyMwagInAl9Y8l068+xIwNqM2JWb2iMXWGziC5fyZF1jGrcDtRIxHU7lLTFVQ\nyLekOuBu4HCIewJ3wYJVcJlB58TsLDN7VgEvm0s9+cp1PTA4hDARGA9cCGwDDM6yUeCT9IDxIYQL\ngdOYT1+e4GRGEOgCHERgH/TTm7UCtjAmZN2Mcpbg99qHAPdDshIKVfAP4JYIHjOzKNMGStnTx2SF\nMrP70zXxl+HD9K8CJ5lZyewgamargOHA8BDC50j4Jm9zPm/RnbZE9EiH83cDRU3ri2oIC7Up0Wcy\nFS85OxiieVBdA+81wCDgnshsVratkzzROnkpOyGE7sC5FOhLws7sRMTBVNMD2C7r1lWQG+DoWq+8\nIps2G/grcA9Er0N1FSyLYSjekR+roXgpBoW8lK0QQjVwItCXwJkYbdiThO4U2BfYNuMG5t0A6LIA\nm6ZxlA16C3gYGA7xRKgqQL3Bowb3AiPMbHXGTZScU8hLLoQQtgfOpsB3SegNBHYlYj+q6YIP6Wua\nacsaAh1mqZZRYwkwAQ/2ByCaAdUFWGXwpMFDwCNmtjTbVkolUchL7qRzDb4CnErgVIxtaZsG/r7A\n3sDW2bYxF4ZD4XWIqOyufD1e5TftsUcLoLoaaiMP9YeAUelSUZFWp5CXXEuH9HsDp1DgDBL2J2B0\nJqELVXTB1+FXckp9ViOAl7yG+g5Zt6UVGT4MPwp4CpJngZVQqIF5Db4R3MPAGM2Ml1KgkJeKEkLY\nAziFwGnAiRhbsW2jYf0vAm0ybWL5GAs8BdOBfbJuS5F9hG/sMAp4EqL5PgwfBXgh9sudkcCrxZw8\nF0I4BrgY33eiI/BVM3u0WO8n+aAldFJRzOx94DbgthDC1sCxLONUJnEGE+hMFQmdMDpTRWdgd6B9\npk0uXY3q1+ct5Bfi1zBjgBEQTUk/K2vgrQZ4AhiVwGgzq2vFZrXDl7reCTzYiu8rZUw9eRHWVvzr\nCpwMHEWBY0jSgrrb08Ae1LA70BmvKlCVWVNLxwfA7V5vtU/WbdkCCTANeAEP9ueh4R2oAaiBDxvg\nKfx42sw+yK6l64QQEtSTl2ZQT16EtVvtvpke16eh3xnoTS29mMrRTOEgoJoqEnbD2IMqdsd7+5W4\nXK9MN6lZhHeHXwJegGQMJEv9szCpgTcavAjdWOCFBnhf69elnCnkRdYj/WB/Pz2GAYQQ2gIHE9Ob\n2fRiLv9Kkm7ys12j3v5u+Ga+22TT9lbTxgc0SjXkDXgXD/RXgVfAJnqFuRqAKi8X/2Ls9XzGAuPq\nzZZl1V6RYlDIizRTWmb3xfQAIISwO9CLpfTmDY7mdQ4mDRHaErEzgV2oYmdYe+xAbob7CwEWZtzP\nNWAB8HZ6TAEmQfwKUJf+S1fDYoOJMUzCM39yDNPMTIV5JdcU8iJbwMzmkNbXBwghbAV0Abqyiq7M\nYT/m0Q2jC5auzi9g7EDE56hhZ3zYe80FQJmt37cqbGHUOgsQl7IuyNccb0A0HcKKdZdN1gbeq/dN\nl9Z04l+NYL6G3aUSKeRFWlBapnRKeqyV3uPfDdiPhK4soiuL6crbdCOh49oXbp32/nekinb4zP72\nsPa/26VHiVTvi9oQPmqB1eArgLnAnCbHbOBdaJgNYUmjz6sa+NhgWuRzKBrn/jurfcRFRFDIi7SK\ntBc5Nz2ebfy9EEI7YF9gP1bSldl0ZS5fINCRhF0w2n3qhG2JaI+xLVW0p/CpC4H2QFv8N7yq0WNL\n3ybYChaswEjLCSV4YC/Hy90u2sjjRxB/BMlcCEubfBZVw9ICzG2Ad82zfg4wEw/y6fVmtS38Nyl5\n6c/JPqwr3bRXCOFAYJGZzc6uZVLKtIROpMSl6/l33eBRoBOBjhi7kGxiVX8AqkjSw9YGf/XaI1BN\nIX0MVAFxekQkxBhResTAYqqrEkJ7iFZCoX4jYwxVUFflBfIWRbAg8bz/GJhHk068ma347P9i+RRC\nOBZ4Dp+G0NgQM/teBk2SMqCQF8mRdAXA5/ALgB2ArTZxtN3o9wNbYdQDq9OjvtGxGq+8tgJfal6X\n/nddejTuvC8xs4bi/u1FpCmFvIiISE6VyPQdERERaWkKeRERkZxSyIuIiOSUQl5ERCSnFPIiIiI5\npZAXERHJKYW8iIhITinkRUREckohLyIiklMKeRERkZxSyIuIiOSUQl5ERCSnFPIiIiI5pZAXERHJ\nKYW8iIhITinkRUREckohLyIiklMKeRERkZxSyIuIiOSUQl5ERCSnFPIiIiI5pZAXERHJKYW8iIhI\nTinkRUREckohLyIiklMKeRERkZxSyIuIiOSUQl5ERCSnFPIiIiI5pZAXERHJKYW8iIhITinkRURE\nckohLyIiklMKeRERkZxSyIuIiOSUQl5ERCSnFPIiIiI5pZAXERHJKYW8iIhITv0/We5MSjy3oZsA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x213391160b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x0['x_7'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2133915c8d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SW5MPARE5GWE8zdiOv1iC32LbAWcB+etBOgOLAg7ImDDqC3wbhbatIfqNiPQLOqJcYkk+\ny4nIFcCL7E6ebbCrgh2AM4HoWpDdgDUBB2RMGLUHvo5B33og40TkhKAjyhWW5LOYiFwFPMh+wPEI\neUFHlKVaorvuZRnQ0YHtDzIm/RoB70XhpCjIKBE5OeiIcoEl+SwlIlcDQ+kNHIztoN9aOwEnATJP\ndBOeF3BAxoRRHvC8wOkCMkJEzgg6orCzJJ+FROSvwAP0AQ7CEny6dASOAfgNLXhvib7mPQJ0BRr4\nb/tRuoPg7cBuQD10U8XBaPvTigxD/4HrAq2Ba4D1ZZ7zB7p208R/XldgUonH70PXd5oBD5T53K+A\nntj/mS0RBZ4ROAfgvyJyTsABhZrtrs8yIjIIuJc+wIFYgq8OXwHvgJ5BvR9sLDnnbTQJ7Aw44Bng\nXmAymtxfBJqiUy8FaLJ9CZgONC7nmC8A5/nH2hetbXw2cAqauAFWAHuiP1QXo4n+N3QtuR0wBeiF\nFlDygCOBb4DOaM3knuilGt236qvPLR5wKXpix1+cc48FG084WZLPIiIyGLibvsABWIKvTh/7b5yM\nJhYTnMZoMt7UgG81OuIfB+xfzudfDvxC6RO2QegMwKf+x9cDX+L3Jd6El4ChwBf+x72Aa4ETgDvR\nKzOGVvylmDIccCXwfwCXO+f+FWw84WPT9VlCRK4D7qYfluBrQn/09zgjgUuCjSVneegJ1jp0BF5W\nMfAo0BCdWi/PfsBEdOQNMAMdkR9Z4jlvAj3QjRk7oCPyJ0o83gWdAfgdmI2O8rugMwjPAn/f8i/L\nlCDAg+jyCf/n7zUyaWRJPguIyBDgLvqjgxVL8NVPgEPwc8d/gJsDDSe3/ADUB2qhJ1ivouvpSW/7\nj9dGE8T76Pp8eU5F1/L7APnoUsD+wHUlnjMD/XfeFXgPnbK/Aq3Yhv/6/0SXcA4D7gJ2AS4C7kHX\nd7oAe6HFX8yWE3Sm5nqAB0TEzqrTyKbrM5yf4P+5IcGbmpVAZ2qnAm4ocFWw8eSEONoqcCUwGngc\nnVZPJvoCYD6wxH9sHDr13qSc432MJvp/AnsD09AEfgFwk/+cWv5jJRP0lcC3wOflHPdZ4A1SJwcT\n/bhPB2aBXdNaSQ4d0Q9zwEnOudEBBxQKluQzmIicBTxjCT5gceB59Pe2exYYGGg4uedgoAOaTDdl\nF3Rj3XXlPN4PXXu5p8R9zwN/IVX8qC06dVNy79cjwD+AuZs45hL0CozP0OT+D2CC/1hT4CN0U56p\nHA843cGoBHiHOOc+CjqibGfT9RlKRPZDeII9cQwIOpocF0M3Yu8IyFno6M3UHI+NL3erzOPr0H/E\nkpK/+pKDnN7odE1Jm+tncA3wV6A5Ot1TXOKxuH+fqbwI8KzA/hGIviUi3YKOKNtZks9AItKGCG/S\nEuFIxNbgM0At9BLqxkDkOGzdtbrcgH5vZ6Nr80PQHe9noMn6RvQaxznoNeznote3n1jiGGf5x0k6\nCngY3UQ5C13DvwU4mtQGl6vRkfid6Ga6F9CNd5dtIsb30Y13yaXjnuju/XfRmYAYOn1vqiYfeDUC\nXWpBbKyItAw6omxW9vTWBExE6hHhbeqxLacQtX+hDFIHzR9PeLBqf3AT2fyublN5i9Bv8nz00rg9\n0I1wB6Cj9V+A/6LT5Y3RBDsevYY+aS56rX3Szeh45mZgHrA9muBL7ojvgW7wux7tgd4O3dR3Spn4\nCtH1/FEl7muBXgJ2DroZ8L/oWaGpuvrAmCj02A4WvSMi+znnVgcdVTayNfkMIiIRhFeJcSTnE2WH\noCMym7QcHeStywf3M1qYxRiTfj8AvRJQ8D54Rznn4kFHlG1suj6z/B3H0ZxoCT6jNUIHm7WKQLoA\nCwIOyJiw2h14JQocCtwdcDBZyZJ8hvAbNQzhEHSzsMlsTfFb1K4D6QSsCjggY8LqEOABAa4RkWOD\njibb2HR9BhCRXgif0pUYx9hGu6wyE62X4u2IFlSpHWw8xoSSA07w4I21kOjqnJsZdETZwkbyAROR\n1kR4i5ZE+JMl+KzTDi1vL/PRime2ZGhM+gnwVARa1IHYaBHJDzqibGFJPkAiEiXCS9Sjge2kz2K7\nAseBVlKzFrXGVI+GwMsxoBvamtBsAUvywboGj735MzG2CToUs1X2AI4AvXb74GBjMSa0egBDI8AV\nInJC0NFkA1uTD4iIdEKYzL7kcUjQ0Zi0+RT4ELQFqZXeNib9HHCig9fWQWIP59yMoCPKZDaSD4CI\nxIgwnEaI1aQPmb74XVFfRmujG2PSS4AnBVrVgtjLtj6/eZbkg3EtHntyPDFrVBUyyRa1e4KWOL1h\ns083xlRFA2B0DLyuwKCgo8lkOZ/kReRSEZkpIgUiMkFEelbz63UB7qAPglVkDidBy6XvBsidwP3B\nxmNMKO0FDBKI3CYiOwcdTabK6SQvIiejv4FvRcde3wFjRaS8xtRb+3p5RHiOJmCd5UIugi7LtwNk\nEPB0sPEYE0q3Ai0Foo+LiF2AvAk5neTR1lOPOuf+65z7BbgIbXV1bjW93hAcXTiemF0ulwOSLWqb\nA3Iu2gDFGJM+dYHHY5DojxabNmXkbJIXkTx0vmdc8j6nlxp8gL91Ks2vtydwC30Rmqf76CZj5aNd\nUrcH5ATg40DDMSZ8DgFOcxAdJiLbBx1NpsnZJA80QftRLixz/0KgWTpfyJ+mH872OPql88gmK9QB\nBgINHEQOAiYHHJAxYTNUoF49kKFBR5JpcjnJ16QL8ejMcTZNn7PqAWcDdRMg+6DV8Ywx6dEUGBYF\nd7qIWOWREnI5yS8BErBRU9cdSGPvUBHZlgh30A1n0/Q5riElWtR2BeYHHJAxYXIW0D8Bscf85VhD\nDid551wxMBE4MHmfvzvzQOCLNL7UYISG7G+tZwy6Nj8QiCVb1K4IOCBjwkKA/4tCog1wTtDRZIqc\nLmsrIicBz6C76r9Gd9v/GejonFuchuO3QJhOb2px0NYezYTKLLRFbaIZ2q/WWtQakx6nOhi9EOLt\nnHOFQUcTtJwdyQM450ah1ZLuAP6Hthk5NB0J3nc7tYjRJ01HM+HRFr9F7QKgE9ai1ph0uV3A2wGr\nKw3k+Ei+OolIB2AqhxJJ/wV5JjSmoGXu6Yp2sMvp825j0uQ84L/LIN7aObc26GiCZL9Rqs9NbINH\nj6DDMBmtC3AkaLHFA4KNxZjQuBmgEXB5wIEEzpJ8NfBH8WfS1xrQmC3QE3/75yfA8cHGYkwotAX+\nIhAdIiINg44mSJbkq4eO4vcKOgyTNfoCvUFL354fbCzGhMINQLQeuqE6Z1mST7MNo/h+Noo3lXQQ\n0B3gSeC6YGMxJus1By6PQPSvIlIv6GiCYkk+/S6iNp7+sjamEgT4E9AZkHuAe4KNx5isdzngbQOc\nGnQkQbEkn0Yikk+Ec+lmo3hTRRHgOGAnQK4Dngg2HmOyWhvgCA9il+VqK1pL8un1JzwasWfQYZis\nFkOvoW8ByAX419gZY6rk4gjE90C3uOYcS/LpJJzPjsQ3qoZvTGXlA6ejZXAjJ1KiI7IxplIOA1rE\ngYuDjiQIluTTRERa4DiMvazPnEmTUi1qDwW+DTggY7JRFLg0BpHTRKRR0NHUNKt4lyYiMoQof+da\nIllfhvwz4Ge0T18e0Ard+d2kxHN+RnPOH0ABWv2/WQXHnQy8hm4wS/63iwE3lXneKuAD4DegGGgM\nHAMbuvh9TqqFUG9gvxKf+zswBr0KLSynsCvQpfm1eeB+BHYOOCBjss1CoKWD+DXOuWFBR1OTbNSZ\nBiIiRLiQzkjWJ3iA2cA+aFL10IQ7HLgMNmwoLAJaozvB36zEsWujG16TSb7sVpgC4CmgHXAmUBdY\nio5qQX9WP0ansh3wPNABbSftAW8BRxOeBA/aovZs4IliWN8V3DSwvsXGVMIOaKGpVy4TkQddDo1u\nw/SrMEj98GhL95C0kz0DLaW+PfqzcSywEh21J3UF+qO7wCv747INUM9/26bMY+OBBqRG7g2B9miB\nStDZhR3Qglbt/NtL/Mc+9+8PY/5rgrbLjhWAdHKwPOiIjMkyFwnE26NDmJxhST49zqUhcdoEHUY1\nKURH3HUqeuIWKAKGAg8AI4BFZR7/FU3So4B7gUeAiSUeb4qO7Fei09jL/PuWocsBYS7/viN6AhZZ\nKdARWBdwQMZkk35AwzhwVNCR1CRL8ltJRBognEx3YiEZx5fmgHfRqfmmW3ms5Nr6qcAJ/rGfRNfg\nk5YD36Aj1zPRi17eQfu3gM4uHAj8F3iO1F6Bt4CD0XX8h4FH0WWHsGkDnALIInStxFrUGrNlosDR\nMcjLqQYRluS33uE4atE16DCqydvAYuDPaThWK3SavxmarE5Gp+tLjtQdOpI/wH/eXv5byY3lPdB1\n/cv8xyYDtYCW6P6AU4BDgNFAIg1xZ5qd0ZMkZqF1cL0gozEmixwNFHcUkZ2CjqSmWJLfegPYjmIa\nBB1GNXgbHRmfDdSvhuNH0US+rMR99Si9ix//45XlHGMt2rztcGAeOluwHbpen0Cn9sNod/xJxyno\n5ghjTMUOAWIeOTRlb0l+a0U4hJ1CWMT2bWAqutmrokaNVV2m8NA1+ZKtI1qzcWJeCuWeRI0F9gW2\n9Y9XclBb9uOw2QtdrmA8ug5ijNm8+sD+QPTYoCOpKZbkt4KINMejHW2DjiTN3kIHiCegldfW+G/F\nJZ5TACxAk7RDd7gv8J+X9Cp6+V3SJ8B0dN19PvAKOkIv2cynF3qt+2foCP97YBKw9ybinO4/J/lY\nCz+O39Dp/QgbzwqETR//jTeAc4KNxZiscGwEvL650mferpPfOgMAQpfkv0VH58+Uuf8YoJt/eyqp\nwjZCqrx6f5LfFU3gJUf5Beia+Rr0evnmwHnoZrqkFuha/QfoSUEjtCpllzKxFKMb8k4scd+26LT9\n6+j/7OPIjf/hB6Lf24nPoGsV9wcajjGZ7Sjg0ij6m+XFgIOpdlbxbiuIyGM05mwuD+F0vckuHjoz\n8gPAP4EhgYZjTGbbPQ4/Pu+cOzvoSKqbTddvjbCux5vsk2xR2wGQG9BrCI0xm9YnBnm9go6iJliS\nryIRaYlHm9BN1ZvsFUWXOloCchHwUrDxGJOx9gKKdxaRsjU3Q8eSfNUNAMK3Hm+yWx5a178pICcD\n7wcbjzEZqQdo/gtrhZMNLMlX3QCaEN+o9roxQauNtqht5CByGFpC0BiT0hnIc/jZPswsyVdVlP1p\nlxN7t0022gatcbCNB9Ib7Q1sjFH5QJcEOm8fapbkq0BEoiRoXerSL2MyTQO0WmHtYpDuaAECY4za\nOwb5od98Z0m+anYEYhVWgjMmaI3REX1eIUgnStcQNiaX9QCKOoR9850l+arRprKW5E02aIbfonY1\nsCvWotYY8EttRoA9Ag6kWlmSr5q2QPn11I3JNK3RFr+RJcBuWItaY9omb7QMMIhqZ0m+atpSizi1\ngg7DmErogN+idg565VCYu/cYU5GGQL6HFtgOLUvyVdPGpupNVuqMttTmJ6BvsLEYEygBdkhgSV6J\nyBQRuVlEWlVnQFlBaMd2dvmcyVLd0bbafAEcGWwsxgSqRQTdSB1alRnJdwauBGaKyLsicoKI5Gai\ni9De1uNNVtsPfyA/Bq2cY0wuahWFqK3Jl7AH8GegCG3R94eI3Cciu6U9sgwlIhE8Wtp0vcl6BwA9\nAYYDVwcbizGBaA5EQz07XdkkH3fOveacOxrdrzsUXeH7QUS+EJFz0x5h5mmKI8+SvMl6AhwOdAEY\nBvw90HAMwL+BdkAdoBebL0m8AG1UsCvaneiaCo79Ivor//gy9z+P/jpvDPy1zGOz/OOvqTj0rLQj\nkGgWdBTVqTJJvlTjeefcfOfcnc65XYADgenAQ+kMLkNpeq8TcBTGpEMEOBbYGZCbgYeDjSenjUST\n7O3A/9ArIA4FlpTz/PVoJ6KbgW4VHHsWcC3Qr8z9S4ELgAeA94Dn0CWcpEuBe4B6W/g1ZJvmQKKe\niNRN1xFFJCIifxORGSKyTkSmichN6Tp+ZVUmyUt5DzjnPnbOnUnIdykaE0pR4CSgFSCXosnG1Lyh\nwL7ArcCewGS0reBT5Ty/jf85Z6BjsGH4BV5KeBWt7NYBWARMAeaWeHwGUAudBTgEaESqz8EItJ7C\nYMI7kq/MmalXAAAgAElEQVSfvJHOqnfXA38BLgE6ot/AwSJyWRpfY4tVJsk/CxRs7gnOuVVbF05W\ncCX+NiYc8oDTgB0AORUYG2w8OacY+BYYT2ok3w1YAXxSweeuRBNz60081hhN8AcAv/i3J5JqQdzY\nf40rgJeA34BC/75b0CQf5pF8bKMbabAv+g0+HZiETo94wGFpfI0ttsVJ3jl3jnNu9ZY+X0RODWlN\nYEvvJpxqA2cC2zmQI4AJAQeUS5YACXS9fCA6AHwE7Zb2QwWfexF6drapK8Ei6KWSI9G1/k7AtujJ\nBOh0fSPgv/5xOqH/EQYBvdE91reje65frtJXltnykjfSmeQnoXNjtdD1luPRf9xAzpzFuerJWSKy\nCujmnJtRLS8QEBHZGfiVs9CfGWPCZA2a28dX9MRcJJW4vbn7NvWYQ0fQeWhiT96/1n9s23KOUYSu\nzQup8UejEh8vQ6eka/n3Lfc/p5H/Oh56gtEIXbdZhm44KiRVEbEBerKwDGhCuGqoFaHfE3Z2zk1L\nxxFF5C50XqwFmtwjwI3OubvTcfzKqs7r3Mtdw89yNpI34bEY+A6YDtElOK8YcWiqKQ42sgzkyrmd\nTsVs+jtfmZXQshv1Vm7iOcvLfFyyO2HZ9feSz11ciTiySjrz1enomdFXwC7AauAmEfnDOTc8ja+z\nRXKzmM3WsSRvspOHlq2fAsyG2DKI+4O1ZsD+IH3RGjmr0MlamjeHP/4AQASSE3916kDjxhCLwapV\nsHYtrqgIqaaJwQpI8s2lbpf8pe3QL95R/T++gv5ajfrv80rcLvs+UuLj1egmuOQIvDO6c/5T/xgH\n+M+P+u8nov+YZUWBs/3nxNG1dQ+YBvzov14CLZDQEt2E94P/3Fb+569BOxW2Rc8Au6FT+BPQZYSm\npL6fJd+X/B6X/Z6X/bjsYyXvZzOPb+IxKfl5zvn36/9WSX5Oyc8veXu9+IdM53+MFug3dBxwMbA3\nem3kXWhRihplSb6qLNWbTBcHpqK/23+H2KpU77ld0LTRB03qZbdsbRg3nnkmbL89ctsduDWrqE1t\n6lOfZUWL+f132HZb6NsX+vRBunfXpD9/PsycCXPn6vnBokWwbJmeDKxeDevXg5fW3jgbfmlnwOyh\no/zReITSJyPAhhuJEp8P+o/2I6nHSl7WtjkJ4En/dpTUSUaBf9vzP/4JKHDwo2jiboBu/EuemOxP\nak/GDqQGpl3Q6/fz0SWA/DJvZe+r6DlpmPov/bu49FpIhb+n3wb+BOntvyzAbOfczf7H34nI2egl\nEzWuOtfkVwNdQ7gmvxMwnYHATkFHY0wJhcD3wFSQ+RBZp7/yY+hvlwFoQt8P3VNdEYlE4Pzz4dRT\nNSsPG0bkzTHUIp8LuRCAd3mXmdHfKEp45OdDr17Qp4++r19/88dfuhRmzIA5c/RkYOFCvW/lSj0Z\nKCzExeObTdyOjEjsYZM8EfE28b1NnqiUHD1vjQh6UpF8y/ff1wLynb6vJfq+tv+2pScQW3Li8QZ6\n9QDbOefKrmFUiZ/7BN189yN6XeOzAM65bTfzqdXCknwliUg7YAZnAu2DjsbktOXopdTTIboIXJH+\n2q2DTrX3R5N6T6AqlT7yYjHixx4Ll16aunPuXBgyBObNoxOdGMQg2tCGL/mSV3mVnyI/uAJvvUQi\nsMceOsrv3Rt22KHqX+aaNTozMHs2zJsHCxbAkiWwYoWeDKxbpycDwSwVmJonQKTk0gxstDxTdvp/\nc6IeJGo759KyDUVEXkR/BOPo2sYf+JdPOOf6pOM1KhVPZZO8iOzvnPuonMf+4px71L/9A3C4c27u\npp6brfwufHM4DZ3zNKamzENH6jMhthTi/gxvY3SU3g+dft+D9KzDNYhE3KoBA4Sbb974wdGjiTzy\nOJJIcCqncgZnUItaAExlKiMZyST5llWsxjnYaSfo109H+TvtpOv76VZUpCcCM2fqycD8+bB4MSxf\nricDW7BvwGYGclOBcy6dFe96AJ8DtwGjgH2AR4ELnHMvput1tjieKiT59Wj52huSZz4i0gR4Gujj\nnGuU9igziIjkAYX8iQg9go7GhJaH1iX5EZgDsZUQ939U21J6Pb091ZOZWgNzu3aFYcM2/YQ1a+Cm\nm5DvvmcHdmAQg9iLvUo9ZSELGclIPudzlkQW4Xmw/fbQv7+O8Lt0gWi0GoLfDM/Tk4CS+wYWL07t\nG1izpjr2DZgMNt85l5ZqrSJyK1qyEFI/luuBS5xz5ZUurFZVSfL7oZUT1qDXArZDd3pMBQY652an\nO8hMIzH5nV604OCgIzGhUYRucv4FZB5E1+pcXwToAm5/kD5oYt+Kme9K2ROY3KKF47nnNn8OMWEC\nkb/9A2/dGg7iIC7lUhpuooPTWtbyKq8yjnH8Hp1NPOHYZhsd3ffuDT166K79TOF5qX0Dc+fqicGi\nRRvtGyCR2OxhbHYg8/3knOucjgP5Sf4EtJ9L8t897pxbVv5nVa8qrcmLSD20HNOf0d9DNwP3uOpa\n4M8wEpGP2I0BnBR0JCZrrUavTvoNIgtwrEc8dEvQPqTW03tRorp2DTsMGFu3Lrz9dsVP9jy47z4i\n77xHHWpzCZdwOIcj5eS3OHE+4APe4i2mRaeyPhEnFoOePTXp77cfNMyiTo+rVpXeN7BwYel9AwUF\nuOJiS/YZaqxzLi0lZ/0kf4xzrmwTgcBUNcl3B15Al/6aoz0ML3fOrU1veJlJRJ6gGWdxkV2CaLbQ\nQjSpz4DYElwirlfwNiCV0Puio+f84KIs5Vx0DY733oO8vAqe7Zs9WzfmzZ/P7uzOtVxL603WVC9t\nIhN5mZeZEpnMGq8AEejUSdfxe/eGFi225ivJHOvXw6xZ+m2aOze1iXDpUj0hKCiwKwpqWDHwpHPu\n4nQczE/yg9CrUAuBL4EhQe5Nq8p0/fVoMePH0N6FHdAL/LcFznDOfZnuIDONiNxILW5jiCV5swke\n2tlzCjAH8pZDsb++2wK9AjmZ1Hclc4uE3oG/uDhqlC6kV8aIEUSfeBo8jzM4g9M4jfwtPH2ZyUxG\nMpJv5CuWswLnoFWr1Dr+rrtWz8a9IE2eDNdcU/rrcg66doXu3WHsWJ0t2H77DcWHKCxMFScyVZYA\nbnLO3ZWOg4nIoWg3n6loM4Hb0IHw7kENgquS5OcD5zrn3ilxXx7wT+AK51yt9IaYeUTkVOAFrkcv\n2zS5LY42AfsRmAex1XqXoGVGkkm9D1pjLFs8jzYx5ZFHNLNW1urVcMMNyA8/0oxmDGYw3Srse17a\ncpYzilF8wicsiswn4UGjRqkRfrduWz7JEEaep5sGZ85M1RsouW9gzRrbN7AFTnHOVUt/ZRFpAMwG\nrnbOPV0dr1FhDFVI8k2cc2WLIycf6++cq6gvYtYTkV7Al1yE1gM1uWUdeinbr37RmYJU0Zke6OVs\nfdCiM9l8qckk0L3y//wn7Ltv1Q/0+edE/nEnXsFaDuVQLuZiGtCg0ocpoog3eIOxjGV2dAbFCY/a\ntXX9vndv2Gcf2CaMfS/TZOXKTe8bWLlSZwe2YKkgrLo75/5XXQcXka+B951zN1bXa2z29XNkr1xa\niUhTYCEnA7sFHY2pdktJNXFZBF6xDn22QZN5f/99T8I1sbMO/RoZNAiOPHLrDhaPw733EnlvHHWp\nw2VcxiEcUu7GvIp4eHzCJ7zBG0yN/khBophoVEf2/fpp4m/SZOtCzlWFhbpvYNYs+P331L6BkvUG\niotDVW+gXnVNpfub1OcAtzjn/lUdr1FhDJbkK09EBGEdB1Ob/YKOxqRdsonLLL/ojL+evj2l19N3\nR6uAh5lEo3D22XDGGek54PTpyA034hYtpCtdGcQgWqZhEWMKUxjNaP4XmehWe2sFYOedU+v4bdqE\nbx0/aPG4bh5MngzMn69LBcuXb6g34IqKkAyvN7DQOZe2+VgRuRd4E52ib4HuX9sD6OScW5qu16lU\nTJbkq0ZiMpXu7MJWDnBMwBLoFpmfgLl+Exf/R6I9pZN6W7JreJIO+bGYKz7qKOGKK9J74OHDiT4z\nHPEcAxnIKZxCHulZXJ/HPEYxii/4nGWRpXgeNGuWSvidOtV8AZ7NeeEFeOIJOOGE0hWEyyouhmef\nhQ8+0MI9TZrAwIFwmH/x12efwfPP61R8PA4tW8JJJ8HBJep5vP++vlZhIRx6KFxySeqxBQtg8GB4\n9NH01ivwPE3+ZfcNLFu2od6AW78eCWDfgAeMcc4dla4DisgI9NdFY7Qv73i0l/zMdL1GpWOyJF81\nIvIczTjZLqPLMuvRUfovup6eLDoTBbqSauLSGx2557pGIqzo1w9uuy39B1+xAm68EX76mZa0YDCD\n6UKXtL7EGtbwEi/xER8xPzqXeEIb5/id8+jeHWoFuFX4l1/gjjt0L0G3bptP8jfeqEnxvPO0A/DS\npbq7vrNfxuW773Q6vXVr3Yz4xRfwn//AXXdpoaGVK+Hkk/UKx2bN9P3gwdpMCOD66+FPf9LvS1BW\nrEg1LUruG0heXpisN5DGSwzjwN+cc3dsfeSZyxJU1Y1jAadTgHYEMZlpJamiMwsBv4lLbWBfUteo\n74O//mxKaegcK5ZW0yxjw4bw73/DJ5/wx133cEXhFRzBEVzERdRPUwmgetTjHP9PUaKId3mXd1a/\nwwdjf2PMmAT5+bD33pr0e/XS1rk1paBA9zQOGgTDK+gy/vXXMGWKjvrr1dP7yjb96dq19McnnKCX\n3k2Zokl+/nz93P799fFu3TSZ9uoF48bpiUGQCR70v0T37vpWDgFYt670voHkJsLly5Fk06It2DcQ\nA75J71eQeSzJV92HgF4PbZvvMscCtDPbTIgtSTVxaYQm9GQTl26QpsnhcGsMzFqyyYtp0qd/f7ze\nveHOO3n3w3f5jPFcyRUcwAFV3pi3Kfnkc7T/xyUcX/EVrxS9wqQvvmf8+PWIlO6c16yar5wZNkwv\nWujeveIk/8UXehXjiBFam6hOHd1ceO65kF9O+YGJEzUBJpN/y5Y6TT9tGjRtClOn6n7KNWvg6afL\nb1GQierW1WWXTp3KfYqANi2aOzdVfCjZtGj2bMQ/d/2tZiIOjk3XbwWJyhx60Iojgo4kR3nAdLTm\n+1yILU+tp7dCm7gkr0/fhdxbT0+HI4ExtWvDO+9U+Ny0+O035IabcEsW0Z3uXMM1tKD6y939yq+M\nYhQT5RtWsgrnoF1b6NdfR7ft26d3496HH+r6+aOPQiwGV18NHTqUP11/3XVaMKdHD12HX7kShg6F\nPffUKfektWvhxBN1/T4ahauuSq3ZA4wfrwm9qEjX6gcOhHvv1a+vQwf417/0mvqBA1Mj/jC67z4Y\nO5Zfi4tdFQpAZBcbyW8Nj7FM5yxsUFgzitGCMz+jRWfWpIrOdEY3ySWbuKSlpZShFejwb/36mlm8\n3nln3Esj4emnmfzcCM72zuYczuEkTiJWjb+udmEXbuImcLCYxYxkJONnjee5OQt59lnd5JZslbvH\nHlu3cW/xYk2m99+vCX5LeB5EInDTTalNcZdcolslrroqNZqvW1c31hUUwKRJuhqy446p0XyfPqWn\n5CdP1jXwK67QCyhuuUWnzC++WKfzG1S+nEFWmDyZ4nicTbZMDxtL8ltnHEs5n9UE10UkzNaiU+/a\nxAUKdfCeB+xNaj19X6hCaRWzJdokbyxfXv3z1yWdcw7eMcfg3XADT0x9grGMZTCD6UxamoVt1vZs\nz2X+n3XeOl7ndd5f8j5vvD6LV15x1K2r0/l9+mhDncruRJ86VUfiF16YKkvrefD99/DaazodX3bW\noHFjPdEo+Vpt/H+cxYtTtf1FdFMe6Oh89mxdxy+7Xg862n/wQd3QN2+ejuC7+PseW7WCn37auhpI\nmWrFCpg3jzzg06BjqQmW5LeOngnOgjRvCs5Ni0kVnVmM8/wmLvXRZN7Pf78X2q3NVL+dkzeWLavZ\nJA+w3XbwyCO4Dz/k93vu47L1l3EUR3EhF1KPejUSQl3qcqr/x0t42jlv3Vt8+uHPvP++ds7bay9d\nx993Xw25InvtBU8+Wfq+u+/WXfGnnbbpZYHdd4dPPtFJldp+xaU5c/S5m2sr4HmazDdl+HCtEtih\ng67Tl7yELR7Xzw2jSZM23LQkbzbPObdQojKVGexqSb6SPGAuWh52lr+e7v9S2QHYHyS5Sa4zmdvE\nJew6Jm8sC6wdNhxwAF6fPvCPf/D2p2/zKZ9yJVcygAFp3ZhXkQgRDvH/kIBJTOLl+Mt8/81kvvpq\nHQC77Zaa1m9ZTo2fOnWgbdvS99WurTv7k6Pzxx/X3eJDhujHBx4Izz2nJwNnn62j0ccegyOOSE3V\nv/CCbs5r3lwT+4QJek391VdvHMOsWfDxx/o6oCcYkQiMGaO9AebOhY4dN/68MPj4Y1w0yuR43P0e\ndCw1wZL81vJ4j+nshK3Lb14C+AVdU//dLzrjP7QLpZu4tNn0EUwAdkneWL48yDA0k91+O94vv7Dq\nxpu5Y9kdvMu7XM3VNAuogUR3/w8ezGY2IxnJVz9P4LFflvPoo5rkkwl/1101iZan7Oh92TKdhk+q\nU0c3yD30EFx0ka6VDxigu+uTCgt1+n3xYv12tW6tU/Gb2kD3wAO6yS+5zSI/Xzf3DRumo/grr9Ql\ngrApKIAJE3CJBC8GHUtNsd31W0lEjgFe40qyuxtJuhWgRWem+k1c1qWauHQjtUmuN3qZlslckWgU\nd+aZcNZZQYeS8vjjREeMIuKE8ziPP/NnohlSZHgFKxjFKD7lExZE/yCRgIYNdKd+snNeeZe9mer1\n0UdafAjYKcgqdDXJkvxWEpGGCIs5mFhO17Ffjm6S85u4OL/oTB00kSebuOwN1A0uSlMFtWIxV3TE\nEbLJed8gJeezp02jLW0ZzGB2y7CiFUUU8SZvMpaxzIpOpzjhUauWrt/36aNr4vVqZnuBAW65BffF\nF3wXj7s9g46lpliSTwMRGUljjucyYjlzMfY8dJPcLL+Ji79ppzGp0rB90c4MtiaU3ZqIsLRPnw1D\noIzz3ntE7h+KV1TIcRzHeZzHNhlYv9DDYzzjeY3X+CXyoyvwiiQa1Z3vyQI8m9tEZ7ZOQQEccwxe\ncTFDnHP3BB1PTbEknwYichDwPucCrYOOphp4aF0ov+hM3koo9v/btEWLzvRBk3p7rOhM2LQHZnTs\nqIXQM1VREdxxB5HPv2RbtuVqrqYvfWt0Y15l/ciPvMRL/E8mspo1OKc73ZONdNq2tc556ZSLU/Vg\nST4tRCRChFnsQSuODTqaNChCE/rPIH+kmrhEgC7gBoAkN8ntsJnDmHDYB/i6aVMYOTLoUCr2ww/I\nzbfgViynF724mqtpStOgo6rQAhbwIi/yBZ+zNLIEz9Pa9MmE37lzZnXOy0a33or7/HO+j8ddt6Bj\nqUmW5NNERG4kyh1cS4TaQUdTSatJNXFZgGM94qHXou9DquhML6zmTy46BngjP1+7nWQDz4PHHiMy\n6mViLsIFXMBxHJcxG/MqsoY1vMIrjGMcf0TnEk846tfXNfzevbW0bZCd87JRian6G5xzdwcdT02y\nJJ8mItIc+J0jEXoGHU0FFqJJfQbEluASftGZbSndxKU7YJuAzaXAw6AXUaez0Xh1W7RIN+bNmEF7\n2nMt17Ir2VWqPE6csYzlbd5mRvRX1icS5OVp57w+fXQDX1hLz6bTmDFw7704oH0uTdWDJfm0koi8\nyQ4cllE95j20It8UYA7kLUutpzendBOXjljRGbOx+4FBoNVYWlR/s5i0GzOGyLD/wxWv5wRO4FzO\npU6W9oeewARe5VV+jHzPWq8QEa2G16+fjvJ33DHoCDOPc3DeecRnz+a9RMIdGXQ8Nc2SfBqJyNHA\n6/wFCOqHLQ78hDZx+R1iq1NNXHYltUmuD37zEWMq8DroVpOHHkoVN882hYVw223IV9+wHY24hmvY\nL8uveZ3OdEYykm/ka1ayEue0Yl5yHX/nnW3jHsB332kTH+AQ59z76Ty2iFwP/BMY5py7Jp3HThdL\n8mkkIjEi/MFebE9NnS+uQ0vDTgVZAJGCVNGZHujlbH2A/bBaPaZqfsOvfHf77TpkzGbff4/cfCtu\n1Qr60IcruILtyf7r1pawhFGM4jM+Y3FkAQlP6+gnE37Xrlve8S5s/GvjpyUS7OrSmPBEpCcwElgJ\nfGRJPkeIyD/I5zoGEa2WBe2l6Hr6NIguBq8YHLANmsyTTVx6Qtbt/zOZKQ7kiWg/0mNDcPmI58HD\nDxN55XXyXIy/cCFHc3TWbMyrSCGFvMZrvM/7zI3OpDjhqFOndOe8ujlSkWr+fDj9dJxzXOacezhd\nxxWResBE4GLgZuB/luRzhIjsBExL2wa8Oeh6+iy/6IzfxGV7UvXe+wK7Q0h+RZlMFIlGcaedVrpY\nerZbsACuux7mzGYXduFarqUDHbb405/1/5TUmtY8wzPlfs5kJvMwDzOLWezADpzO6RzGYRsev5qr\n+Y7vNvq8XvTin/wTgPd5nyd4gkIKOZRDuYRLUl8SCxjMYB7lUepQBw+PD/mQN3mTX6M/U5goJhaD\n7t21AM9++21Z57xs9eCD8OabLE8kaOmcW5eu44rIs8Bi59wgEfmIDE7yOTqBU32cczNE5EU+4US6\nEatU25oE8CvaxGWu38TFPwdrT+kmLu2wojOm5tQCV7h8ebj+yzVrBs8+A2+8wbT/e5gL4xdyEidx\nFmdt8ca8drTjfu7HoT+om5sNWMAChjCEYziGm7iJiUzkPu6jCU3oQQ8A/sbfKCbVG3YlKzmf8xnA\ngA0f38/9DGEIzWjGEIbQne70ohcAwxjGhVy4If4IEQ7y/5CA7/iO0fHRTP52El9/vY7779duc8mN\ne61DVMxr+XJ46y28RIKhaU7wp6AtOHqk65jVyZJ89bidNZzCJPRC8/KsR0fpv/hFZ9alis50JZXU\ne0MIVg1NNquXSEhhkO1mq9PRR+MddBDceiujvh3FOMbxV/66IXFuTpQoDWm4RS/zOq/TnOZcxEWA\njvp/4AdGM3pDkq9H6UL24xhHbWrTH20lN5/51KPeho+70Y05zKEXvRjHOPLIow99yo2hq/8HD+Yy\nlxd5ka9+mcATU5fx2GPaprZ/f53W79hx853zMt0rr4DnsR74d7qOKSItgWHAQc654oqenwksyVcD\n59xUERnOp5xG9xKj+ZVoE5dpEFkI+E1caqOFZgago/RekIGVt00uawQsWbLEEdYJpLp19ULqiRNZ\nevsdDFk9hP7053Iup/Fm+iT+zu+cyInkk08nOnEBF5RbYe8nftLWtCX0pCf/3kwOeod3OJADqYVW\nv2lJSwopZBrTaEpTpjKVIzmSNazhaZ5mGMO2+EtuRSuu5VoAVrgVjGY0H//xMaNGzWPECL3+vm9f\nTfh77pldnfPWrIFXXiHheTzsnEvn2ele6JhrksiGaxeiQD8RuQyolc7Nfelga/LVREQ6AFNpR4R1\nEFuSauLSiFQVub7ovI81ozeZrDfwRePGMHp00KFUP8+Dhx4i8vpb1CKfi7iIP/EnImWqSHzN1xRQ\nQGtas5SlPMMzLGUpT/HUJqf7BzKQwzmcUzl1w31f8RU3cAPv8A75ZXbq/szPXMZlPMzDpYr4jGc8\nT/M0RRRxMAczkIHcy720pz0d6MC/+BcJEgxk4IYRf2UUUcQYxvAO7zArOo0iv3Ner32gdx/o1Qvq\nZ3jpy4cfhpdfptDz2Mk5Nz9dxxWRbYA2Ze5+Br1o+S7n3M/peq10sZF8NXHOTRORD5nJQS2Ag0g1\ncdmFsA6HTFg1A1i5UiuLhP3i60gErroK78QTKbj+eob+PpR3eIfBDKYd7TY8bW/23nC7He3oSEdO\n4RQ+5mMO5/CtDmMMY2hHu42q9PXx/yRNZjIzmMEVXMEZnMEt3EJDGnIxF9ONbjSgciXx8snnWP+P\nl/D4nM95ff3rfD1+ivvk0yKJREp3zmuaYa0B5s6Fl1/GeR5/S2eCB3DOrUUrkWwgImuBpZmY4MGS\nfHW7OgKTzoK8fwQdiTFboTVAPA5r1+ZOA/QWLWD4cHjlFX59+FHOT5zPKZzCQAZumD4vqR71aEUr\n5jFvk4drRCOWUXrmeDnLqUvdjUbxhRTyER9xHudtNsRiinmQB7mRG5nHPBIk6IIWLGpFK37iJ/Zl\n38p81aVEiNDX/4OH/MIvjPRGMmnyRCZPXs1DD0H79rpxr08faNcu+HPA//wHT4T5wNAaesmMng7P\n4m0Vmc8594MH99wH3tyggzFmK7RN3gjr5rvNOf54vNdextuzKyMYwUAG8g3fbPS0AgqYx7xy1/A7\n05lJTCp13zd8Q2c6b/Tcj/mYOHHdFb8ZwxnOPuxDBzrg4ZEgseGxOHE8vC35CrdYRzpyK7fyunuD\nF9wIjuVYVk3fnmefhfPOg5NPhn//W6vMJRIVHy/dJk6EL78kkkhwjXOuoCZe0zl3QKZePgeW5GvC\n3R6suDHDz/aM2ZxdkjeWLw8yjODUqwcPPIC7+y6WbFPgBjOYsziLz/iMBSzgB37gZm4mRowDOACA\nx3mcO7lzwyGO5mjmM59HeZQ5zOE1XuNTPuVETtzo5cYwht70pv5m+j7OYhYf8zHncA6gu/UjRBjD\nGL7kS+Yyl450TPM3IqUZzbiSKxnFKF733uRczqXu4ja89qpw1VVwzDFw990wfrxWFa5uiQQ89BDx\naJQJwEvV/4rZwTbe1QAR+QvwyLf8f3v3HmdTvf9x/PXde3MkCV1Ux6UQJVREJUqXwzm/btRPp+RS\nEilJOCm3HF1VpFIhKlQyR3KQ3H6hUpFLLimSZIaMy7iNMZe11uf3x3cPQy5jzMzae83n+Xjsx+zZ\ne+29PuNh5r2/63uzQzOVije/E23N9+8P11/vbzF+8zwYMgQ+m47BTqMrRzlqUYsOdODc6MYVgxhE\nMskMYciBly5nOW/yJr/zO2dxFm1pS1OaHvL2iSRyH/fxMi//aTR+Tl3pyr3cy5U55ul+x3cMZSgO\nDhCHRZcAAB14SURBVA/wQL6MDThRDg6zmc00pvFreM2BnfPq1z+4c16Z3M06PCFTpsCr9gJ9fRFZ\nnP9niE8a8oXAGBOJwIqaUH0xhHUkvYo3HhA2Bh55BO680+9yYkNiIjz5JGzezCVcQk96cv7Bjg0V\ntYhFTGISq0LLD+ycV7PmwQV48mNjw9RUuOce3NRUPhCR+07+HYNDQ76QGGPqGVg0AEL9/S5GqTwI\nRyJ4//wndOjgdymxJSGB0MjRGNelFa1oTes/DaRT1nrWH9g5bxe7ELGr7GVvpFO9et4G7r39Nkyc\nSLrnUVVENud/5fFLQ74QGWMGhqHv92Au97sYpU5QyXBY9jdtanjiCb9LiT2pqdC3L2b5Cs7hHHrS\n85iX2hWkkEICCXzJfLbm2Dkvu4V/6aVQLBeXPdesgc6dERH6isjzBV95fNGQL0TGmOIRWFIdLl4G\nYf2sr+LJOUDylVfCiy/6XUrs+u47Qs88h5eWyt/4Gw/zcK6XvS3K0klnClOYxSw2htcf2Dnv6qtt\nP36DBnDqEZYBzcyEBx/E2bSJ1a7LFfGy1Gxh0pAvZMaYSw0s7g2RZ/0uRqkTcDHwc5UqwujRAV8N\n5yQ5DrzyCqGZcziFEnShC81ohtElsHLFw2Me85jCFNaEV5PuZhEO26V1sxfgOSM6S3HECJgwAUeE\nuiKy0t/KY5OGvA+MMX1DMPA7MPmxG61SheFa4KsyZeDTT/0uJT789hs81RuSt1CHOvSgB5UI0DZv\nhWQlK/kP/+GH0FLZ6+0zYPvuL78cEhIQEXqLiF5eOgoNeR8YY4pFYFEVqLUcIiX8LkipXLgbmBAO\nw6xZ8b09WWH76CPCo98Hz6MNbbiHe3RgXh4lkcQEJrCAr9kd2gWQGF2f3vG7tlilv6k+EJEsB1qv\nA572uxilcqki2BVH9u71u5T40qoV7qcTcS+5iDGMoT3tWc5yv6uKSxWoQA96UI8rRDyT5nm00IA/\nNg15n4jIjx70fRnkG7+LUSoXqmTfKaqr3p2M0qVh2DBk4L/5o8QuutGNQQxiD3v8rizuzGIWc5hj\nBOkoIkv8rifWacj7a3AYvm8JTrLflSh1HAeWti2K69fnl8aN8aZOhptuYhazaE1rZjMb0VWvc2UT\nmxjCENdgPhCRD/2uJx5oyPtIRBwH7tgKu24HN8PvguLQC0ADoDRQHmgBrD3Ccf2B84CSwN+Adcd5\n3zHYX45w9Gso+tqc/p3juexbzcOOeSVa1zmQY3FTayFQH/J5C5GCc2AbFQ35kxOJQJ8+eKNGknrW\nKTzP8/Sgx1F3r1NWBhkMYIDr4CQJ8rDf9cQLDXmficgmB275HrxOdrdudQK+Ah7FBuYcIAtoCuTc\nfmoQMAwYCSwCTgWaAZnHee/TgS05br8f4ZhaQHKOY77O8dxK7JiLBGA80Bf4MfqcC3QGRhA/v4Tn\ngB1wp5fr80fVqkjCBLj/flaEVnEf9/EBH5CFTvU+nIfHC7wg61nvuLh3iIgODMmlePn7EmgistCD\n9mPAFNYGyEExHWiDncNdG3gf2Ajk7Kh7DegH3IIN5bHAZmDycd7bAGcBZ0dvZx3hmMhhx5TL8dzP\nwKXAdcD1QJ3oYwAvRR+PtzXRwqGQtuTzW9u2uJ8k4FxUjXd5lwd4gJXolO+c3uVd5jMfD+8eEVl6\n/FeobBryMUJEPgAG9QT53O9i4tgubDhnh+1v2Bb2jTmOKQ1cCXx7nPdKxe68VgloDqw+wjG/AH8F\nqgKtgcQcz9XGdh0kYa8C/BJ97Fdsd0A8LoZUQkS0JV8AypSBt99Gnu7Ppr/soCtdGcxg9qIN1hnM\n4EM+BOglIrpIwwnSkI8tfQx83hLcn/yuJA4J0A1oxMG+8S3Y0C9/2LHlo88dTQ3gXWAK8CG237wh\n9gpAtquwVw5mAsOxHyiuBfZFn78IeB64Cfg78CJ28NpD2Jb859jQr4ftdogHpV3XsGOH32UEV5Mm\neFM+hSZNmM50WtOaL/iiyA7M+4EfeIVXBBiFHeKiTpAuhhNjjDGlI7CwIlRbDJFyx3+JiuqMDdwF\nEN3R27bWG2HDOWfQ/xP7CXd8Lt/bwXYJtMIOuDuS3UBl4FXg/qMcMwb7weFt7AeJJdjuhXuBDUCs\nb0NcC/ixcmV4//2Te6MVK2DCBFi7FnbsgGeeseuVgp2LP2oULFoEmzdDqVJQty507HhwPdOjmTcP\n3nsPtmyBChXsa648uN/6Mc+bbcIEewO4+264666Dz61eDa+/Dm+9VfALAq1ZA336wo7tXMEVdKf7\ngb3qi4JEEnmIh9x00r/y8JrquvR5oy35GCMiexy4eSPsawmervKQO12w/fPz4JA/g+dgW/iHT1FM\njj6XWxHgco49Kv90bEv9aMdsBwYCb2AHCtbAzj1vgh0weKRZAbHmbMifgXfp6VC1Kjz22J/3Fk1P\nh3XroG1beOcdGDjQ7t3et++x33PVKnj2Wbj5Zvu6a66Bfv1gw4bcnRdg/Xr7AaZ/f/vad9+1y9OC\n/fAxdCh07144K/7VqAET/wNt2rDU/EA72jGe8TgE/6/CbnbTi15OBhm/eXh3aMDnnYZ8DBKR9S60\nmAvyGBTRC3W51wX4LzAX/rQy+AXYMP+/HI/twYZswxM4h4cdLX+sdlQqNuCPdkx3oAd2Kp8Lh4yh\ndqKPxbpzwa54555ktQ0aQPv2douxw68mnnoqvPyy3WS8QgW4+GIbymvXwrZtR3/PSZNsq/2uu+wm\n5e3bw4UXHrrW/rHOC7Bxo/0QcNlldnH0KlXsYwAff2z3P61e/c+vK0jt2+NNnEBW9QsYyUg60IHV\nRxwhEgxZZNGPfm4yyaku7t9FRAeBnAQN+RglInMFOr8F9EKD/mgexvaZf4SdGpccvaXnOKYbdpDb\nVGxQtwUqALfnOKYd0DvH988As7H97Muwl9M3Ah1yHPMv4EvsoLpvsHP0iwH3HKHO2diBd9mTe+tj\nR9rPwE7ti2Bb9rGuMthw3FPIK7WlptqvpUod/ZjVq+1l/Zzq17eP51aVKvaqwbZt9pL/pk32sU2b\nYOZM+wHBD+XK2S3X+vQhsfhWHuERhjKUVFL9qaeACMJgBssqVomHd6uI/Op3TfEu4ncB6uhE5B1j\nTMmXYWgIu/CLblZ5qOHYf5Mmhz3+HjbMAZ4A0oBO2NH3jbGD3nJuEZKIXfgm206gI3ZwXlns4Lhv\nsYPpsiVh++h3YKfRNQK+Aw7vNU4HumLny2f7K/ay/f1ACey0vr8c96f134GlbVNSoGzZwjlpZiaM\nHAk33ginnHL0445UU9myJzblr1Il6NABevSwl/MffBAqVoSePaFTJztOYMwYu6BNly5Qp07efqa8\nuukmvGuvhWefZepXU5nPfLrRjWu5Nu63shWE4QxnJjMNcL+IfH3cF6nj0pCPcSLymjEmNAiGhLEt\n0vj+Vc5fuV0tbkD0djRfHPb9EP68Qt3hcjtorwRwpNkS7aO3eHLgQ05Kir2sXdBcF/79bxu4jz9e\n8OcDuPVWe8s2YwaULAk1a9pxAiNGwNatdtDe+PE28AtT8eIwcCDe6tXs7tuPATsH0IAGPM7jnHNC\nI01ihyCMZCQJ9qPwo9EpxSof6OX6OCAirwI9n0d3rVP+OrBsb2HMlXddGDDABurLLx+7FQ/2kvbh\nde3caR/Pq927Ydw46NoVfvrJturPO8/22TuOvbTvl5o1kUmfwD33sMQspR3tSCABNy5GdxwkCKMY\nxcd8DNBNRIb5XVOQaMjHCREZDDzxDEefwqVUQSsHdmR5Qa96lx3wmzfD4MFw2mnHf03NmrD0sMXQ\nFi+2j+fVm29Cy5Zw5pngeTbYc9boxcDOAx074iaMJ7NqRd7mbTrSkTWs8buqXBGE93iPj/gIoIeI\nvOZ3TUGjIR9HRORl4MkB2IFhSvkhkh/r1+/fb6fJrYtOOPzjD3t/61Ybnv37wy+/QJ8+NlhTUuwt\nZ8i+8IKdKpftzjvh++8hIcGOiH//fTsiv0WL3J33cIsX2wF3zZvb72vUsC33RYtg6lQIh23LPhac\neaZdW6BXLzYU20xnOvMGb5BGmt+VHVV2C34c4wCeEJHj9ZCpPNDFcOKQMaY38NyzQB+/i1FFzmmh\nEKk33GADOK9++MHONz98rnrTptCuHbRqdehzIvb7IUPsNDawry9fHnr1Onjc/PkwejQkJ9vpd506\n2WlzuTlvzvfJzLSD7p5+2o6uzzZ9un3/4sXtGIGc7x0rMjNhwABC3y7kdE6nO91pRCO/qzqEIAxj\nGJOYBLYFrwFfQDTk45Qxpi/wzPPAU34Xo4qUikDSZZfBq7qdUkxbuRLT72lk904a0pBudOOsI26z\nVLhcXIYwRKYz3QCdRWT4yb6nMeYh7KKX50cf+hEYKCIzTva9451ero9TIvIsMKA3dpGV+Bpqo+JZ\nOUDXr48DtWsjkyZCy5Z8ZxbRhjZ8wie+DsxzcHiBF2Q60wHa5UfARyVilxSpi53x+gXwX2PMxfn0\n/nFLW/JxzhjziIE3/gfkYwgdY6kQpfLF34A5pUrZfmkVH5KT4cknYcMGqlGNJ3iCC7mwUEtIJZWB\nDPQWs1gEuVtEJhbk+YwxO4CeIvJeQZ4n1mlLPs6JyJsC/zMD0huCk+R3QSrw/gqwb9+hg+BUbCtf\n3m7c06MH6yOJdKITb/EW+9lfKKffxCY609lZwpI0Qf5RkAFvjAkZY+4GSnL8HaUDT0M+AERkhgtX\n/QRbrwBnmd8FqUCrBHYg3K5dfpeiTtQtt+BNnYw0qM9EPqENbfi2gHPwB36gE53cP/gj0cO7QkRm\nF8R5jDG1jDF7gQzgLaCFiPxcEOeKJxryASEiKx2otwNWNARvit8FqcCqln2nMBbEUfmvRAkYNAh5\ndTAppzn0pjf96c92tuf7qaYxjR70kP3s/9LFrSciBTmB/2fgUqABdjfnscaYi479kuDTkA8QEdni\nQONMmNwcu6+5jrhQ+e3ARjoFvSCOKliXXYZMngQtWrDAfEMb2jCZyfkyMM/FZRjDGMxgPLy3Pbxm\nBb2bnIg4IrJeRJaJSB9gOfBYQZ4zHmjIB4yIpHnQUuDF7thdz7TnVOWnA8OVNeTjXygEXbvifTiO\n9Ipn8xqv8TAP8yt53/wtlVSe5ElvEpM84BERecSn/eBDxMe+TwVKQz6ARMQTkaeADiPA/Qd4emFV\n5ZfSYFd708v1wXHuuTB2DDz2GOsiv/MgDzKCEaQfsmnz8W1iEw/xkLOUpfsEaSYibxVQxYcwxjxv\njGlsjKkc7Zt/AbgOKPIb3WjIB5iIjBZoOhdSa4GzwO+CVGBEjNGWfBA1b47330lIvbpMYAJtacsi\nFuXqpUtZSic6uVvYkj3Abk4BV5vT2cAYbL/8HOxc+aYicvgGk0WOzpMvAowxlcLwsQdXPQ2mD7rH\nsDo5ZUIh2d2kiaFfP79LUQVlyRLMgIFI6h6u53q60IVy/HlHPxeXsYxlHOPEYOZ6eP9b0P3vKve0\nJV8EiMhGF64VuzO3NAF3o99Fqbh2uucZtuf/aGwVQ+rVQ/77Kdx2G/P5kta0ZipT8Ti4814yyXSl\nqzuWsSLI0x5eUw342KIt+SLGGNMoAgmnwNnvQfhOvwtScakusOy88+DDD/0uRRWGxER4qjdsSqIm\nNelJTzaykZd4yc0gI9nFvUtEtEcwBmnIF0HGmLIheMeDOztip9qV9LsoFVf+AcwoWRI++8zvUlRh\nmjiR0PB3EDcLQTCYSYJ00NZ77NLL9UWQiOz0oCXw4CjIuByc5X4XpeLKXwHS0uy2pqroqFED74wy\njiAO8KIg2v8e4zTkiyixRnlw+Xr4uT54b6CL56jcOT/7ji5tWzRkZsKIEdC1K+zYsQS4WESeEr0U\nHPM05Is4EfnJgfpZMKwrcD24BbnupAqGqtl3dBpd8K1bBx07OkyY4ABP4rrXiMg6v8tSuaMhrxCR\ndBF5DGi2AJJqgdcfCml/KhWPdNW7IiAzE8aNg4ceEpKS1iBST0QGiYh/G9KrE6Yhrw4QkVkOXOzA\nc8+BUxOcmX4XpWLSgV0/dNW7YPr2W2jXzuG99zxcdxCuW09EVvhdljpxGvLqECKyX0T6e1ArEb7+\nO3AXyGa/C1MxpQRgwmFtyQdNYiL06uXRuzds3foVIrWjfe8Zfpem8kZDXh2RiKxx4Qag9aew80Jw\nX4d82J9KBUUxY0Rb8gGRlmYH1t1/v7BkyWbgDjzvRhFZ7Xdp6uRoyKujio7A/9CBamkw8jGQeuB+\n73dhKiaUdF2jLfk4JwKzZ8O99zokJGTguk/jutVF5FMdOR8MGvLquERkp4g8DFz9I6y+EngE0DZc\n0VZWBLZt87sMlVdr10KXLi7PPw+7d0/G82qIyDMiomNuA0RDXuWaiCx0oK7A4yNgfyVwnwdS/S5M\n+eIM0D75eLRrFwweDJ06wZo1vwA3iOe1FJHf/S5N5T8NeXVCRMQRkaEuXJAKb/YD53xwXgd0ZE7R\nUh50dH08cV2YNAnuvddl+vS9wKO4bm0Rmet3aargaMirPBGRZBF5zINqKTC2G0gVcN4FHL+LU4Wi\nIkBGBqSn+12KOhbXhblzoX17hzfeENLSRuN5VUVkmIjor2vAacirkyIiv3siDwjU3AKfPgBcBE4C\n5NiQUgVR5ew72pqPTY4DM2bY+e4DB0JS0nzgChHpJCI6mKKI0JBX+UJEfnZF7gLqboA5/wQuA2c6\nuh5+UF2YfUf75WNLZiZMmQKtWjkMGgR//DEdaCCue5OILPW7PFW4In4XoIJFRJYB/zDGNPoJBt0M\nDa8G9wUIX+d3cSpf1cy+oy352LB/v93696OPHHbuDGPMJOA5cV1dqa4I05BXBUJEvjbGNAKafg+D\nmsClDcF9AsK3opeQgkBb8jFi3z6YPBkmTHBITQ0h8gHwonie7jWlNORVwYkupjHTGDMLuH0R9GoO\nV1UF5wmItAFO8blGlXcR7NK2oi15f+zebUfLT5zosn+/IDIKeElEfvO7NBU7tEGlClx05bzJWSJX\nAw1/g6mdQCqAMxDQEUDxqziItuQLWUoKDB8Od93l8cEHGaSlvY7I+SLSWQNeHc7oyoXKD8aYasDj\nIXggDMXuhdCjQF2/C1Mn5ExgxzXXwLPP+l1KsInAypUwbZowd67geel43mvAUBHZ6nd5KnZpyCtf\nGWPOADpEoKsD510NbjcItwCK+V2cOq5qwK81agjDhxu/awmkXbtg5kyYMsVh8+YI4fDvuO5wYISI\naD+JOi4NeRUTjDER4LYIdHOgcXlwHoFIW3LMx1Yx5ypg4VlnQUKC36UEh+fBkiW21b5gAXieC0xE\n5B1gnojoEhQq1zTkVcwxxtQBuoShjQslrgX3PgjfCZT2uTZ1qBbA5OLF7aIrRhvzJ2XbNvj8c5g6\n1WH79gjh8Fpc921gnIjs8Ls8FZ805FXMMsaUAlqE4T4Xrv8LSAsw7cDchE4NiQWPAsPAzs8uWdLn\nauKQ68J338G0aR4LFxqMycDzxgPvAN/pdq/qZOnfSZVnxpjGwL+AesC5QHMRmZJf7y8iqcA4YJwx\npkIG3PsJtP8Yqp8JTrvo5fw6+XVCdcIuyL6TkqIhfyI2b4bp0+Gzzxx27YoQDq9AZDgi40Vkj9/l\nqeDQkFcn41TgB2A0MKkgTyQiScAgY8xLQN3t0OY1aDsYytYC536ItALOKcgi1J9Uy76zcydUqOBn\nKbFvyxb48kuYN8/lp5/ChEL78LwxwChxnGV+l6eCSS/Xq3xhjPHI55Z8Ls5ZDGhmoJ2B5gLhm0Du\ngNAtgEZOwfuVaNAPGADX6cLFf5KUZIN97lyHdesiGJMFzEAkAZgkIml+l6iCTVvyKm6JSBYwDZhm\njCkLtJwLrefANZ0hVBuc5hC5FdufoCs/5b8LwA640wVxLBH49VdYsADmzXPYsCFCKJSOyGfAfxCZ\nLiJ7/S5TFR0a8ioQonOGRwIjo4HfbBXc+hPc8gyUPhOc26OBfxO2n0GdvBAQCofxivLStpmZsGwZ\nfPMNLFjgsGNHhFAoDc+bDHyC583QFrvyi4a8Cpxo4H8MfBydf99wO9w6FlqMhqrFwLsRuC16Wb+i\nr9XGv+Iikp6SUrTmz6Wk2FHx33wjfP+9R2ZmmEgkEcf5FJiK530pIpl+l6mUhrwKNBFxgC+jt38Z\nYy7Mgltmw+0zodHDEK4Nzs0QaQw0BMr4WnH8Oc11TXrQL9dv3QorVtilZZcuzSIpqRgghMMLcd3J\nwDQcZ7VOeVOxRgfeqXzhx8C7k2WMKQM0A26NQDMHzjRATXCaREO/MXCen0XGgRrA2mrVhHfeCUZr\nXgQSE2H5chvqy5ZlsX27XWU5ElmH43yB/dA4U0S2+1qrUsehIa/yzBhzKnZwtQGWAt2BuUCKiCT6\nWduJMsYYoCrQCGhcDG7IgvMBKkLW9VCscfTJGtgfWFmNgAXlysEnn/hdSt64LqxbZ1vqK1YIy5e7\n7N0bATwikRU4zlzgK+BrEdFNE1Vc0ZBXeWaMuQ4b6of/JxojIu19KClfGWPOAa7Bhv71WVALCJUF\n5zoINwbTEPtgKV8r9df/Ap+EwzB7dnwsbZueDj//bFvpy5d7rFolZGSEMSaTUGgRrjsPG+rfFuRI\neGPMU9iVgS8C9gPfAL1EZG1BnVMVPRrySuWSMeY04GqgcRiaCDTwoDhAJci6HCKXgqkD1MZeFgj7\nWG9h6Q68CjBlCpx2ms/V5JCVZeep//Zb9k1Yt85h69ZiiEAolIrIV4hkj9lYIiIZhVWeMWY6MB5Y\njB0f9QL2M+PFIrK/sOpQwaYhr1QeGWOKY/8o1wbqhOGyEFyaBWcA/AW8S8C7DCLZwV8Huwd7kLwB\ndAUYMwYqVSr8AlwXNm2CDRsOBvqvv2axeXMEz7OXFiKRbXjeCjxvBbAKWAKsEhG38As+MmPMmcBW\n4FoR+drvelQw6Oh6pfIoOkVqafR2gDGmPFA7A2ovhToroa4LF3tQDOAsyLoMwlUhdD52K93K2AEA\n5Ym/RXuqZ99JSSnYkE9Lg+3bbes8O9DXrcsiKSmM49h/tnB4FyIr8bzlwI/YQP9RsrLiYSJ/GWzX\nV8CnKqjCpCGvVD4TkWQgGZiT/Vh0vn41oM42qD0bas+H6i5UdHN06RcDOQ+cKhCuAqGcHwAqA38l\n9n5pL8m+k9dpdK5rX7t9+6G3bdtg+3aP5GSXHTtCZGQc7P0IhVIxZhWum90y/xFYJY6z9eR+Gn9E\nB34OxQ7uW+13PSo4Yu3vhVKBFJ2v/3P0lpDzuehUvspA5Syo/Lu9nf81VBWo7EDZ7GNDIGeBcwZw\nBoTOgHA57AHZt8O/L4ttIhbUL/t59oewQe04tsWdlgb799uv+/bZ+/v22WNseMPWrQ7btgl79tg+\n8oP/IFmEw1sRScJ1NwKbDrutx/M2BWxO+ltATexAT6XyjfbJKxXjolMVK3Hwyn4Fovlt4IyInd9f\nTqCMA6fLUa74nwpuKfDC2MDP/przfhhMsejXCFAMTPQxA5AOXhp4+0HS7PekA8nhcDE8D4739yQc\n3o0xm3Hd37E7C24CNnNoiO8QES/v/2LxxRgzDLgVaCwiG/2uRwWLhrxSARK97FuKozfsS3NIph/1\n/pEeMxzM9f057qdjW6ELgR3AXmBP9OveHN+nRjcVUlHRgL8duE5E1vtdjwoeDXmllPKBMeYt4B7g\nNiDn3PjdIpLuT1UqaDTklVLKB9GloI/0B/h+ERlb2PWoYNKQV0oppQIq3qbkKqWUUiqXNOSVUkqp\ngNKQV0oppQJKQ14ppZQKKA15pZRSKqA05JVSSqmA0pBXSimlAkpDXimllAooDXmllFIqoDTklVJK\nqYDSkFdKKaUCSkNeKaWUCigNeaWUUiqgNOSVUkqpgNKQV0oppQJKQ14ppZQKKA15pZRSKqA05JVS\nSqmA0pBXSimlAkpDXimllAooDXmllFIqoDTklVJKqYDSkFdKKaUCSkNeKaWUCigNeaWUUiqgNOSV\nUkqpgNKQV0oppQJKQ14ppZQKKA15pZRSKqA05JVSSqmA0pBXSimlAkpDXimllAooDXmllFIqoDTk\nlVJKqYDSkFdKKaUCSkNeKaWUCigNeaWUUiqg/h/N0VYlD9dMdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2133916e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x1['x_7'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "def woe_single(DF,Y,X):\n",
    "    if X.nunique()>11:\n",
    "        r = 0\n",
    "        bad=Y.sum()      #坏客户数(假设因变量列为1的是坏客户)\n",
    "        good=Y.count()-bad  #好客户数\n",
    "        n=5\n",
    "        while np.abs(r) < 1:\n",
    "            d1 = pd.DataFrame({\"X\": X, \"Y\": Y, \"Bucket\": pd.qcut(X, n,duplicates='drop')})\n",
    "            d2 = d1.groupby('Bucket', as_index = False)\n",
    "            r, p = stats.spearmanr(d2.mean().X, d2.mean().Y)\n",
    "            n = n - 1\n",
    "        d3 = pd.DataFrame(d2.X.min(), columns = ['min'])\n",
    "        d3['min']=d2.min().X    \n",
    "        d3['max'] = d2.max().X\n",
    "        d3['sum'] = d2.sum().Y\n",
    "        d3['total'] = d2.count().Y\n",
    "        d3['bad_rate'] = d2.mean().Y\n",
    "        d3['group_rate']=d3['total']/(bad+good)\n",
    "        d3['woe']=np.log((d3['bad_rate']/(1-d3['bad_rate']))/(bad/good))\n",
    "        d3['iv']=(d3['sum']/bad-((d3['total']-d3['sum'])/good))*d3['woe']\n",
    "        iv=d3['iv'].sum()\n",
    "        if iv!=0.0 and len(d2)>1:\n",
    "            d3['iv_sum']=iv\n",
    "            woe=list(d3['woe'].round(6))\n",
    "            cut=list(d3['min'].round(6))\n",
    "            cut1=list(d3['max'].round(6))\n",
    "            cut.append(cut1[-1]+1)\n",
    "            x_woe=pd.cut(X,cut,right=False,labels=woe)\n",
    "            return  d3,cut,woe,iv,x_woe\n",
    "        else:\n",
    "            dn1 = pd.DataFrame({\"X\": X, \"Y\": Y, \"Bucket\": pd.cut(X, 100)})\n",
    "            dn2 = dn1.groupby('Bucket', as_index = False)\n",
    "            dn3 = pd.DataFrame(dn2.X.min(), columns = ['min'])\n",
    "            dn3['min']=dn2.min().X    \n",
    "            dn3['max'] = dn2.max().X\n",
    "            dn3['sum'] = dn2.sum().Y\n",
    "            dn3['total'] = dn2.count().Y\n",
    "            while (1):\n",
    "                    if  (len(dn3)>4):\n",
    "                        dn3_min_index = dn3[dn3.total == min(dn3.total)].index.values[0]\n",
    "                        if (dn3_min_index!=0):    #最小值非第一行的情况\n",
    "                            dn3.iloc[dn3_min_index-1, 1] =dn3.iloc[dn3_min_index, 1] \n",
    "                            dn3.iloc[dn3_min_index-1, 2] = dn3.iloc[dn3_min_index, 2] +dn3.iloc[dn3_min_index-1, 2]\n",
    "                            dn3.iloc[dn3_min_index-1, 3] = dn3.iloc[dn3_min_index, 3] +dn3.iloc[dn3_min_index-1, 3]\n",
    "                            dn3=dn3.drop([dn3_min_index])\n",
    "                            dn3= dn3.reset_index(drop=True)\n",
    "                        else:    #最小值是第一行的情况\n",
    "                            dn3.iloc[dn3_min_index+1, 0] =dn3.iloc[dn3_min_index, 0] \n",
    "                            dn3.iloc[dn3_min_index+1, 2] = dn3.iloc[dn3_min_index, 2] +dn3.iloc[dn3_min_index+1, 2]\n",
    "                            dn3.iloc[dn3_min_index+1, 3] = dn3.iloc[dn3_min_index, 3] +dn3.iloc[dn3_min_index+1, 3]\n",
    "                            dn3=dn3.drop([dn3_min_index])\n",
    "                            dn3= dn3.reset_index(drop=True)\n",
    "                    else:\n",
    "                        break\n",
    "            dn3['bad_rate'] =dn3['sum']/dn3['total']\n",
    "            dn3['group_rate']=dn3['total']/(bad+good)\n",
    "            dn3['woe']=np.log((dn3['bad_rate']/(1-dn3['bad_rate']))/(bad/good))\n",
    "            dn3['iv']=(dn3['sum']/bad-((dn3['total']-dn3['sum'])/good))*dn3['woe']\n",
    "            \n",
    "            iv=dn3['iv'].sum()\n",
    "            dn3['iv_sum']=iv\n",
    "            woe=list(dn3['woe'].round(6)) \n",
    "            cut=list(dn3['min'].round(6))\n",
    "            cut1=list(dn3['max'].round(6))\n",
    "            cut.append(cut1[-1]+1)\n",
    "            x_woe=pd.cut(X,cut,right=False,labels=woe)\n",
    "            return  dn3,cut,woe,iv,x_woe\n",
    "    else : \n",
    "        bad=Y.sum()      #坏客户数\n",
    "        good=Y.count()-bad  #好客户数\n",
    "        d1 = pd.DataFrame({\"X\": X, \"Y\": Y})\n",
    "        d2 = d1.groupby('X', as_index =True)\n",
    "        d3 = pd.DataFrame()\n",
    "        \n",
    "        d3['sum'] = d2.sum().Y\n",
    "        d3['total'] = d2.count().Y\n",
    "        for c in range(d3.shape[0])[::-1]:\n",
    "            if ((d3.iloc[c,1]-d3.iloc[c,0])==0) or (d3.iloc[c,0]==0):\n",
    "                d3.iloc[c-1,0]=d3.iloc[c-1,0]+d3.iloc[c,0]\n",
    "                d3.iloc[c-1,1]=d3.iloc[c-1,1]+d3.iloc[c,1]\n",
    "                d3.drop(d3.index[c],inplace=True)\n",
    "            else:\n",
    "                continue\n",
    "        \n",
    "        d3['min']=d3.index  \n",
    "        d3['max'] = d3.index\n",
    "        d3['bad_rate'] =d3['sum']/d3['total']\n",
    "        d3['group_rate']=d3['total']/(bad+good)\n",
    "        d3['woe']=np.log((d3['bad_rate']/(1-d3['bad_rate']))/(bad/good))\n",
    "        d3['iv']=(d3['sum']/bad-((d3['total']-d3['sum'])/good))*d3['woe']\n",
    "        iv=d3['iv'].sum()\n",
    "        d3['iv_sum']=iv\n",
    "        d3=d3[['min','max','sum','total','bad_rate','group_rate','woe','iv','iv_sum']]\n",
    "        \n",
    "        \n",
    "        woe=list(d3['woe'].round(6))\n",
    "        cut=list(d3.index)\n",
    "        x_woe=X.replace(cut,woe)\n",
    "        return d3,cut,woe,iv,x_woe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>cust_group</th>\n",
       "      <th>y</th>\n",
       "      <th>x_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>x_5</th>\n",
       "      <th>x_6</th>\n",
       "      <th>x_7</th>\n",
       "      <th>...</th>\n",
       "      <th>x_148</th>\n",
       "      <th>x_149</th>\n",
       "      <th>x_150</th>\n",
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       "      <th>x_152</th>\n",
       "      <th>x_153</th>\n",
       "      <th>x_154</th>\n",
       "      <th>x_155</th>\n",
       "      <th>x_156</th>\n",
       "      <th>x_157</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110000</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.604988</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110001</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.012058</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110002</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.565979</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110003</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.316209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110004</td>\n",
       "      <td>group_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.008061</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cust_id cust_group  y       x_1       x_2  x_3  x_4  x_5  x_6  x_7  ...    \\\n",
       "0   110000    group_3  0  0.354167  0.604988  -99  -99  -99  -99  -99  ...     \n",
       "1   110001    group_3  0  0.125000  0.012058  -99  -99  -99  -99  -99  ...     \n",
       "2   110002    group_3  0  0.333333  0.565979    0    0    0    0    0  ...     \n",
       "3   110003    group_3  0  0.208333  0.316209    0    0    0    0    1  ...     \n",
       "4   110004    group_3  0  0.208333  0.008061  -99  -99  -99  -99  -99  ...     \n",
       "\n",
       "   x_148  x_149  x_150  x_151  x_152  x_153  x_154  x_155  x_156  x_157  \n",
       "0      1      1      1      1      1      1      1      1      3    -99  \n",
       "1      1      1      1      1      1      1      1      1      2      2  \n",
       "2      1      1      2      1      1      1      1      1      2      2  \n",
       "3      2      1      1      1      1      1      1      1      2      4  \n",
       "4      1      1      1      1      1      1      1      1      2      1  \n",
       "\n",
       "[5 rows x 160 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "           ...   \n",
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       "Name: x_81, Length: 15000, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['x_81']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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    {
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     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\tile.py:227: RuntimeWarning: invalid value encountered in less\n",
      "  if (np.diff(bins) < 0).any():\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Bin edges must be unique: array([-99.      ,        nan,        nan,   0.502722,   2.      ]).\nYou can drop duplicate edges by setting the 'duplicates' kwarg",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-75315604b5b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mcol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'_'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0miv\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mwoe_single\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0miv_all\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-30-d7f6a39f1fa7>\u001b[0m in \u001b[0;36mwoe_single\u001b[0;34m(DF, Y, X)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mcut1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdn3\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'max'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mcut\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcut1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mx_woe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwoe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m             \u001b[1;32mreturn\u001b[0m  \u001b[0mdn3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwoe\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_woe\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[1;32melse\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\tile.py\u001b[0m in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates)\u001b[0m\n\u001b[1;32m    232\u001b[0m                               \u001b[0minclude_lowest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude_lowest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                               \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                               duplicates=duplicates)\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     return _postprocess_for_cut(fac, bins, retbins, x_is_series,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\tile.py\u001b[0m in \u001b[0;36m_bins_to_cuts\u001b[0;34m(x, bins, right, labels, precision, include_lowest, dtype, duplicates)\u001b[0m\n\u001b[1;32m    330\u001b[0m             raise ValueError(\"Bin edges must be unique: {bins!r}.\\nYou \"\n\u001b[1;32m    331\u001b[0m                              \u001b[1;34m\"can drop duplicate edges by setting \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                              \"the 'duplicates' kwarg\".format(bins=bins))\n\u001b[0m\u001b[1;32m    333\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munique_bins\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Bin edges must be unique: array([-99.      ,        nan,        nan,   0.502722,   2.      ]).\nYou can drop duplicate edges by setting the 'duplicates' kwarg"
     ]
    }
   ],
   "source": [
    "iv_all = []\n",
    "for i in range(1,158):\n",
    "    print(i)\n",
    "    col = 'x'+'_'+str(i)\n",
    "    X = train[col]\n",
    "    iv =  woe_single(train,train.y,X)[3]\n",
    "    iv_all.append(iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\tile.py:227: RuntimeWarning: invalid value encountered in less\n",
      "  if (np.diff(bins) < 0).any():\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Bin edges must be unique: array([-99.      ,        nan,        nan,   0.502722,   2.      ]).\nYou can drop duplicate edges by setting the 'duplicates' kwarg",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-023ae048bfcb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwoe_single\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_81\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-48-15021edf78c0>\u001b[0m in \u001b[0;36mwoe_single\u001b[0;34m(DF, Y, X)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mcut1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdn3\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'max'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mcut\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcut1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mx_woe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwoe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m             \u001b[1;32mreturn\u001b[0m  \u001b[0mdn3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwoe\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_woe\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[1;32melse\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\tile.py\u001b[0m in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates)\u001b[0m\n\u001b[1;32m    232\u001b[0m                               \u001b[0minclude_lowest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude_lowest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                               \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                               duplicates=duplicates)\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     return _postprocess_for_cut(fac, bins, retbins, x_is_series,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\tile.py\u001b[0m in \u001b[0;36m_bins_to_cuts\u001b[0;34m(x, bins, right, labels, precision, include_lowest, dtype, duplicates)\u001b[0m\n\u001b[1;32m    330\u001b[0m             raise ValueError(\"Bin edges must be unique: {bins!r}.\\nYou \"\n\u001b[1;32m    331\u001b[0m                              \u001b[1;34m\"can drop duplicate edges by setting \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                              \"the 'duplicates' kwarg\".format(bins=bins))\n\u001b[0m\u001b[1;32m    333\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munique_bins\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Bin edges must be unique: array([-99.      ,        nan,        nan,   0.502722,   2.      ]).\nYou can drop duplicate edges by setting the 'duplicates' kwarg"
     ]
    }
   ],
   "source": [
    "woe_single(train,train.y,train.x_81)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15000,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>total</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>group_rate</th>\n",
       "      <th>woe</th>\n",
       "      <th>iv</th>\n",
       "      <th>iv_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-99</th>\n",
       "      <td>-99</td>\n",
       "      <td>-99</td>\n",
       "      <td>234</td>\n",
       "      <td>7689</td>\n",
       "      <td>0.030433</td>\n",
       "      <td>0.512600</td>\n",
       "      <td>-0.430815</td>\n",
       "      <td>0.078564</td>\n",
       "      <td>0.137551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>455</td>\n",
       "      <td>7271</td>\n",
       "      <td>0.062577</td>\n",
       "      <td>0.484733</td>\n",
       "      <td>0.323774</td>\n",
       "      <td>0.058966</td>\n",
       "      <td>0.137551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.086065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.137551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     min  max  sum  total  bad_rate  group_rate       woe        iv    iv_sum\n",
       "X                                                                            \n",
       "-99  -99  -99  234   7689  0.030433    0.512600 -0.430815  0.078564  0.137551\n",
       " 0     0    0  455   7271  0.062577    0.484733  0.323774  0.058966  0.137551\n",
       " 1     1    1    2     40  0.050000    0.002667  0.086065  0.000021  0.137551"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe_single(train,train.y,train.x_3)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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